1932:. In contrast to many newer methods, it features a well-defined cluster model called "density-reachability". Similar to linkage-based clustering, it is based on connecting points within certain distance thresholds. However, it only connects points that satisfy a density criterion, in the original variant defined as a minimum number of other objects within this radius. A cluster consists of all density-connected objects (which can form a cluster of an arbitrary shape, in contrast to many other methods) plus all objects that are within these objects' range. Another interesting property of DBSCAN is that its complexity is fairly low â it requires a linear number of range queries on the database â and that it will discover essentially the same results (it is
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1828:, a canonical problem in the operations research and computational geometry communities. In a basic facility location problem (of which there are numerous variants that model more elaborate settings), the task is to find the best warehouse locations to optimally service a given set of consumers. One may view "warehouses" as cluster centroids and "consumer locations" as the data to be clustered. This makes it possible to apply the well-developed algorithmic solutions from the facility location literature to the presently considered centroid-based clustering problem.
2043:
2018:. Eventually, objects converge to local maxima of density. Similar to k-means clustering, these "density attractors" can serve as representatives for the data set, but mean-shift can detect arbitrary-shaped clusters similar to DBSCAN. Due to the expensive iterative procedure and density estimation, mean-shift is usually slower than DBSCAN or k-Means. Besides that, the applicability of the mean-shift algorithm to multidimensional data is hindered by the unsmooth behaviour of the kernel density estimate, which results in over-fragmentation of cluster tails.
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internal criteria in cluster evaluation is that high scores on an internal measure do not necessarily result in effective information retrieval applications. Additionally, this evaluation is biased towards algorithms that use the same cluster model. For example, k-means clustering naturally optimizes object distances, and a distance-based internal criterion will likely overrate the resulting clustering.
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kind of structure exists in the data set. An algorithm designed for some kind of models has no chance if the data set contains a radically different set of models, or if the evaluation measures a radically different criterion. For example, k-means clustering can only find convex clusters, and many evaluation indexes assume convex clusters. On a data set with non-convex clusters neither the use of
1760:â to be specified in advance, which is considered to be one of the biggest drawbacks of these algorithms. Furthermore, the algorithms prefer clusters of approximately similar size, as they will always assign an object to the nearest centroid. This often leads to incorrectly cut borders of clusters (which is not surprising since the algorithm optimizes cluster centers, not cluster borders).
1462:, is based on the core idea of objects being more related to nearby objects than to objects farther away. These algorithms connect "objects" to form "clusters" based on their distance. A cluster can be described largely by the maximum distance needed to connect parts of the cluster. At different distances, different clusters will form, which can be represented using a
1470:" comes from: these algorithms do not provide a single partitioning of the data set, but instead provide an extensive hierarchy of clusters that merge with each other at certain distances. In a dendrogram, the y-axis marks the distance at which the clusters merge, while the objects are placed along the x-axis such that the clusters don't mix.
2950:: Purity is a measure of the extent to which clusters contain a single class. Its calculation can be thought of as follows: For each cluster, count the number of data points from the most common class in said cluster. Now take the sum over all clusters and divide by the total number of data points. Formally, given some set of clusters
1493:("Unweighted or Weighted Pair Group Method with Arithmetic Mean", also known as average linkage clustering). Furthermore, hierarchical clustering can be agglomerative (starting with single elements and aggregating them into clusters) or divisive (starting with the complete data set and dividing it into partitions).
5388:. It also may be used to return a more comprehensive set of results in cases where a search term could refer to vastly different things. Each distinct use of the term corresponds to a unique cluster of results, allowing a ranking algorithm to return comprehensive results by picking the top result from each cluster.
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another. An algorithm that is designed for one kind of model will generally fail on a data set that contains a radically different kind of model. For example, k-means cannot find non-convex clusters. Most traditional clustering methods assume the clusters exhibit a spherical, elliptical or convex shape.
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The
FowlkesâMallows index computes the similarity between the clusters returned by the clustering algorithm and the benchmark classifications. The higher the value of the FowlkesâMallows index the more similar the clusters and the benchmark classifications are. It can be computed using the following
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for evaluation. These types of evaluation methods measure how close the clustering is to the predetermined benchmark classes. However, it has recently been discussed whether this is adequate for real data, or only on synthetic data sets with a factual ground truth, since classes can contain internal
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Therefore, the internal evaluation measures are best suited to get some insight into situations where one algorithm performs better than another, but this shall not imply that one algorithm produces more valid results than another. Validity as measured by such an index depends on the claim that this
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The notion of a "cluster" cannot be precisely defined, which is one of the reasons why there are so many clustering algorithms. There is a common denominator: a group of data objects. However, different researchers employ different cluster models, and for each of these cluster models again different
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In external evaluation, clustering results are evaluated based on data that was not used for clustering, such as known class labels and external benchmarks. Such benchmarks consist of a set of pre-classified items, and these sets are often created by (expert) humans. Thus, the benchmark sets can be
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When a clustering result is evaluated based on the data that was clustered itself, this is called internal evaluation. These methods usually assign the best score to the algorithm that produces clusters with high similarity within a cluster and low similarity between clusters. One drawback of using
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External evaluation has similar problems: if we have such "ground truth" labels, then we would not need to cluster; and in practical applications we usually do not have such labels. On the other hand, the labels only reflect one possible partitioning of the data set, which does not imply that there
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These methods will not produce a unique partitioning of the data set, but a hierarchy from which the user still needs to choose appropriate clusters. They are not very robust towards outliers, which will either show up as additional clusters or even cause other clusters to merge (known as "chaining
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There is no objectively "correct" clustering algorithm, but as it was noted, "clustering is in the eye of the beholder." The most appropriate clustering algorithm for a particular problem often needs to be chosen experimentally, unless there is a mathematical reason to prefer one cluster model over
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More than a dozen of internal evaluation measures exist, usually based on the intuition that items in the same cluster should be more similar than items in different clusters. For example, the following methods can be used to assess the quality of clustering algorithms based on internal criterion:
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Neither of these approaches can therefore ultimately judge the actual quality of a clustering, but this needs human evaluation, which is highly subjective. Nevertheless, such statistics can be quite informative in identifying bad clusterings, but one should not dismiss subjective human evaluation.
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Internal evaluation measures suffer from the problem that they represent functions that themselves can be seen as a clustering objective. For example, one could cluster the data set by the
Silhouette coefficient; except that there is no known efficient algorithm for this. By using such an internal
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As listed above, clustering algorithms can be categorized based on their cluster model. The following overview will only list the most prominent examples of clustering algorithms, as there are possibly over 100 published clustering algorithms. Not all provide models for their clusters and can thus
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is that they expect some kind of density drop to detect cluster borders. On data sets with, for example, overlapping
Gaussian distributions â a common use case in artificial data â the cluster borders produced by these algorithms will often look arbitrary, because the cluster density decreases
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This measure doesn't penalize having many clusters, and more clusters will make it easier to produce a high purity. A purity score of 1 is always possible by putting each data point in its own cluster. Also, purity doesn't work well for imbalanced data, where even poorly performing clustering
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data set. In this technique, we create a grid structure, and the comparison is performed on grids (also known as cells). The grid-based technique is fast and has low computational complexity. There are two types of grid-based clustering methods: STING and CLIQUE. Steps involved in grid-based
2676:. Since algorithms that produce clusters with low intra-cluster distances (high intra-cluster similarity) and high inter-cluster distances (low inter-cluster similarity) will have a low DaviesâBouldin index, the clustering algorithm that produces a collection of clusters with the smallest
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family of algorithms) have been adapted to subspace clustering (HiSC, hierarchical subspace clustering and DiSH) and correlation clustering (HiCO, hierarchical correlation clustering, 4C using "correlation connectivity" and ERiC exploring hierarchical density-based correlation clusters).
1844:. This approach models the data as arising from a mixture of probability distributions. It has the advantages of providing principled statistical answers to questions such as how many clusters there are, what clustering method or model to use, and how to detect and deal with outliers.
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To measure cluster tendency is to measure to what degree clusters exist in the data to be clustered, and may be performed as an initial test, before attempting clustering. One way to do this is to compare the data against random data. On average, random data should not have clusters.
4577:. This index scores positively the fact that the labels are as sparse as possible across the clusters, i.e., that each cluster has as few different labels as possible. The higher the value of the Chi Index the greater the relationship between the resulting clusters and the label used.
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A "clustering" is essentially a set of such clusters, usually containing all objects in the data set. Additionally, it may specify the relationship of the clusters to each other, for example, a hierarchy of clusters embedded in each other. Clusterings can be roughly distinguished as:
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The silhouette coefficient contrasts the average distance to elements in the same cluster with the average distance to elements in other clusters. Objects with a high silhouette value are considered well clustered, objects with a low value may be outliers. This index works well with
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Cluster analysis can be used to identify areas where there are greater incidences of particular types of crime. By identifying these distinct areas or "hot spots" where a similar crime has happened over a period of time, it is possible to manage law enforcement resources more
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algorithms can be given. The notion of a cluster, as found by different algorithms, varies significantly in its properties. Understanding these "cluster models" is key to understanding the differences between the various algorithms. Typical cluster models include:
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The Dunn index aims to identify dense and well-separated clusters. It is defined as the ratio between the minimal inter-cluster distance to maximal intra-cluster distance. For each cluster partition, the Dunn index can be calculated by the following formula:
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such that every two nodes in the subset are connected by an edge can be considered as a prototypical form of cluster. Relaxations of the complete connectivity requirement (a fraction of the edges can be missing) are known as quasi-cliques, as in the
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between attributes. However, these algorithms put an extra burden on the user: for many real data sets, there may be no concisely defined mathematical model (e.g. assuming
Gaussian distributions is a rather strong assumption on the data).
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In density-based clustering, clusters are defined as areas of higher density than the remainder of the data set. Objects in sparse areas â that are required to separate clusters â are usually considered to be noise and border points.
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algorithms will give a high purity value. For example, if a size 1000 dataset consists of two classes, one containing 999 points and the other containing 1 point, then every possible partition will have a purity of at least 99.9%.
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1875:, so multiple runs may produce different results. In order to obtain a hard clustering, objects are often then assigned to the Gaussian distribution they most likely belong to; for soft clusterings, this is not necessary.
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Clustering is useful in software evolution as it helps to reduce legacy properties in code by reforming functionality that has become dispersed. It is a form of restructuring and hence is a way of direct preventative
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takes on a value between 0 and 1. An index of 1 means that the two dataset are identical, and an index of 0 indicates that the datasets have no common elements. The
Jaccard index is defined by the following formula:
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Clustering can be used to group all the shopping items available on the web into a set of unique products. For example, all the items on eBay can be grouped into unique products (eBay does not have the concept of a
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1111:. The subtle differences are often in the use of the results: while in data mining, the resulting groups are the matter of interest, in automatic classification the resulting discriminative power is of interest.
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Recommender systems are designed to recommend new items based on a user's tastes. They sometimes use clustering algorithms to predict a user's preferences based on the preferences of other users in the user's
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Clustering may be used to identify different niches within the population of an evolutionary algorithm so that reproductive opportunity can be distributed more evenly amongst the evolving species or subspecies.
1032:. It can be achieved by various algorithms that differ significantly in their understanding of what constitutes a cluster and how to efficiently find them. Popular notions of clusters include groups with small
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Cluster analysis can be used to analyse patterns of antibiotic resistance, to classify antimicrobial compounds according to their mechanism of action, to classify antibiotics according to their antibacterial
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unless constraints are put on the model complexity. A more complex model will usually be able to explain the data better, which makes choosing the appropriate model complexity inherently difficult. Standard
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If the density of a neighboring cell is greater than threshold density then, add the cell in the cluster and repeat steps 4.2 and 4.3 till there is no neighbor with a density greater than threshold density.
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Gao, Caroline X.; Dwyer, Dominic; Zhu, Ye; Smith, Catherine L.; Du, Lan; Filia, Kate M.; Bayer, Johanna; Menssink, Jana M.; Wang, Teresa; Bergmeir, Christoph; Wood, Stephen; Cotton, Sue M. (2023-09-01).
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From poll data, projects such as those undertaken by the Pew
Research Center use cluster analysis to discern typologies of opinions, habits, and demographics that may be useful in politics and marketing.
2142:, which can process huge data sets efficiently, but the resulting "clusters" are merely a rough pre-partitioning of the data set to then analyze the partitions with existing slower methods such as
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Single-linkage on
Gaussian data. At 35 clusters, the biggest cluster starts fragmenting into smaller parts, while before it was still connected to the second largest due to the single-link effect.
1477:, the user also needs to decide on the linkage criterion (since a cluster consists of multiple objects, there are multiple candidates to compute the distance) to use. Popular choices are known as
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A number of measures are adapted from variants used to evaluate classification tasks. In place of counting the number of times a class was correctly assigned to a single data point (known as
6181:; Sander, Jörg; Xu, Xiaowei (1996). "A density-based algorithm for discovering clusters in large spatial databases with noise". In Simoudis, Evangelos; Han, Jiawei; Fayyad, Usama M. (eds.).
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2883:. Since internal criterion seek clusters with high intra-cluster similarity and low inter-cluster similarity, algorithms that produce clusters with high Dunn index are more desirable.
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Remm, Maido; Storm, Christian E. V.; Sonnhammer, Erik L. L. (2001-12-14). "Automatic clustering of orthologs and in-paralogs from pairwise species comparisons11Edited by F. Cohen".
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A confusion matrix can be used to quickly visualize the results of a classification (or clustering) algorithm. It shows how different a cluster is from the gold standard cluster.
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In the process of intelligent grouping of the files and websites, clustering may be used to create a more relevant set of search results compared to normal search engines like
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Bewley, A., & Upcroft, B. (2013). Advantages of
Exploiting Projection Structure for Segmenting Dense 3D Point Clouds. In Australian Conference on Robotics and Automation
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Cluster analysis is used to describe and to make spatial and temporal comparisons of communities (assemblages) of organisms in heterogeneous environments. It is also used in
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The Rand index computes how similar the clusters (returned by the clustering algorithm) are to the benchmark classifications. It can be computed using the following formula:
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In centroid-based clustering, each cluster is represented by a central vector, which is not necessarily a member of the data set. When the number of clusters is fixed to
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R. Ng and J. Han. "Efficient and effective clustering method for spatial data mining". In: Proceedings of the 20th VLDB Conference, pages 144â155, Santiago, Chile, 1994.
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Achtert, E.; Böhm, C.; Kröger, P. (2006). "DeLi-Clu: Boosting
Robustness, Completeness, Usability, and Efficiency of Hierarchical Clustering by a Closest Pair Ranking".
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metric; another provides hierarchical clustering. Using genetic algorithms, a wide range of different fit-functions can be optimized, including mutual information. Also
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are equally weighted. This may be an undesirable characteristic for some clustering applications. The F-measure addresses this concern, as does the chance-corrected
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is the number of pairs of points that are clustered together in the predicted partition but not in the ground truth partition etc. If the dataset is of size N, then
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and these models can usually be characterized as similar to one or more of the above models, and including subspace models when neural networks implement a form of
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Single-linkage on density-based clusters. 20 clusters extracted, most of which contain single elements, since linkage clustering does not have a notion of "noise".
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measure of how much information is shared between a clustering and a ground-truth classification that can detect a non-linear similarity between two clusterings.
5397:'s map of photos and other map sites use clustering to reduce the number of markers on a map. This makes it both faster and reduces the amount of visual clutter.
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Botstein, David; Cox, David R.; Risch, Neil; Olshen, Richard; Curb, David; Dzau, Victor J.; Chen, Yii-Der I.; Hebert, Joan; Pesich, Robert (2001-07-01).
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Kraskov, Alexander; Stögbauer, Harald; Andrzejak, Ralph G.; Grassberger, Peter (1 December 2003). "Hierarchical
Clustering Based on Mutual Information".
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Pfitzner, Darius; Leibbrandt, Richard; Powers, David (2009). "Characterization and evaluation of similarity measures for pairs of clusterings".
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1716:-means often include such optimizations as choosing the best of multiple runs, but also restricting the centroids to members of the data set (
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With this definition, uniform random data should tend to have values near to 0.5, and clustered data should tend to have values nearer to 1.
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Clustering can be used to divide a fluence map into distinct regions for conversion into deliverable fields in MLC-based Radiation Therapy.
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measure for evaluation, one rather compares the similarity of the optimization problems, and not necessarily how useful the clustering is.
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Cluster analysis is used to identify patterns of family life trajectories, professional careers, and daily or weekly time use for example.
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Connectivity-based clustering is a whole family of methods that differ by the way distances are computed. Apart from the usual choice of
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Cluster analysis is used to reconstruct missing bottom hole core data or missing log curves in order to evaluate reservoir properties.
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cluster centers and assign the objects to the nearest cluster center, such that the squared distances from the cluster are minimized.
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Di Marco, Antonio; Navigli, Roberto (2013). "Clustering and Diversifying Web Search Results with Graph-Based Word Sense Induction".
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continuously. On a data set consisting of mixtures of Gaussians, these algorithms are nearly always outperformed by methods such as
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that are initialized randomly and whose parameters are iteratively optimized to better fit the data set. This will converge to a
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This is simply the number of unique elements common to both sets divided by the total number of unique elements in both sets.
2433:{\displaystyle DB={\frac {1}{n}}\sum _{i=1}^{n}\max _{j\neq i}\left({\frac {\sigma _{i}+\sigma _{j}}{d(c_{i},c_{j})}}\right)}
1223:(also known as co-clustering or two-mode-clustering), clusters are modeled with both cluster members and relevant attributes.
986:(in some specific sense defined by the analyst) to each other than to those in other groups (clusters). It is a main task of
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Cluster Analysis: Correlation Profile and Orthometric (factor) Analysis for the Isolation of Unities in Mind and Personality
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K-means has a number of interesting theoretical properties. First, it partitions the data space into a structure known as a
1696:, and thus the common approach is to search only for approximate solutions. A particularly well-known approximate method is
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Hopkins, Brian; Skellam, John Gordon (1954). "A new method for determining the type of distribution of plant individuals".
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is the number of pairs of points that are clustered together in the predicted partition and in the ground truth partition,
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metrics assess whether each pair of data points that is truly in the same cluster is predicted to be in the same cluster.
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point of view, the reproduction of known knowledge may not necessarily be the intended result. In the special scenario of
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Anomalies/outliers are typically â be it explicitly or implicitly â defined with respect to clustering structure in data.
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Evaluation (or "validation") of clustering results is as difficult as the clustering itself. Popular approaches involve "
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However, data containing just a single Gaussian will also score close to 1, as this statistic measures deviation from a
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In recent years, considerable effort has been put into improving the performance of existing algorithms. Among them are
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for core and noise points, but not for border points) in each run, therefore there is no need to run it multiple times.
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Agrawal, R.; Gehrke, J.; Gunopulos, D.; Raghavan, P. (2005). "Automatic Subspace Clustering of High Dimensional Data".
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Clustering algorithms are used for robotic situational awareness to track objects and detect outliers in sensor data.
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Peter J. Rousseeuw (1987). "Silhouettes: A graphical aid to the interpretation and validation of cluster analysis".
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has exactly one negative edge) yields results with more than two clusters, or subgraphs with only positive edges.
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into market segments and to better understand the relationships between different groups of consumers/potential
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and intended use of the results. Cluster analysis as such is not an automatic task, but an iterative process of
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or clusters of organisms (individuals) at the species, genus or higher level that share a number of attributes.
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Luna-Romera, JosĂ© MarĂa; MartĂnez-Ballesteros, MarĂa; GarcĂa-GutiĂ©rrez, Jorge; Riquelme, JosĂ© C. (June 2019).
2165:(where only some attributes are used, and cluster models include the relevant attributes for the cluster) and
1771:. Third, it can be seen as a variation of model-based clustering, and Lloyd's algorithm as a variation of the
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1234:: some algorithms do not provide a refined model for their results and just provide the grouping information.
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or interactive multi-objective optimization that involves trial and failure. It is often necessary to modify
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To find structural similarity, etc., for example, 3000 chemical compounds were clustered in the space of 90
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is a generalization of DBSCAN that removes the need to choose an appropriate value for the range parameter
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Cluster analysis originated in anthropology by Driver and Kroeber in 1932 and introduced to psychology by
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is a family of corrected-for-chance variants of this that has a reduced bias for varying cluster numbers.
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Cluster analysis is for example used to identify groups of schools or students with similar properties.
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that also looks for arbitrary rotated ("correlated") subspace clusters that can be modeled by giving a
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problem. The appropriate clustering algorithm and parameter settings (including parameters such as the
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1037:
572:
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163:
125:
120:
80:
75:
6677:
6183:
Proceedings of the Second International Conference on Knowledge Discovery and Data Mining (KDD-96)
5888:
Estivill-Castro, Vladimir (20 June 2002). "Why so many clustering algorithms â A Position Paper".
3476:
2917:
structure, the attributes present may not allow separation of clusters or the classes may contain
2014:
is a clustering approach where each object is moved to the densest area in its vicinity, based on
1971:
1943:
1435:
11068:
10681:
10621:
10558:
10196:
10180:
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6120:
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1961:
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of the covariance matrices, that provide a balance between overfitting and fidelity to the data.
1796:-means separates data into Voronoi cells, which assumes equal-sized clusters (not adequate here).
1767:. Second, it is conceptually close to nearest neighbor classification, and as such is popular in
1467:
1458:
1451:
1152:
519:
368:
268:
95:
7049:
5075:, making this statistic largely useless in application (as real data never is remotely uniform).
4876:
4816:
2867:) between two clusters may be any number of distance measures, such as the distance between the
1434:
not easily be categorized. An overview of algorithms explained in Knowledge can be found in the
1280:, edges may change sign and result in a bifurcated graph. The weaker "clusterability axiom" (no
978:
is the task of grouping a set of objects in such a way that objects in the same group (called a
11271:
11106:
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1705:
1127:
699:
675:
577:
338:
313:
273:
85:
7593:
Hartuv, Erez; Shamir, Ron (2000-12-31). "A clustering algorithm based on graph connectivity".
7298:
Fowlkes, E. B.; Mallows, C. L. (1983). "A Method for Comparing Two Hierarchical Clusterings".
6669:
Proceedings of the 2004 ACM SIGMOD international conference on Management of data - SIGMOD '04
5850:
Cattell, R. B. (1943). "The description of personality: Basic traits resolved into clusters".
3836:
3773:
2230:" evaluation, where the clustering is compared to an existing "ground truth" classification, "
11131:
11073:
11016:
10842:
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10644:
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9513:
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9144:
8967:
8846:
8831:
8796:
8484:
8384:
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7098:
6833:
5942:"An overview of clustering methods with guidelines for application in mental health research"
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2150:
1868:
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475:
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19th International Conference on Scientific and Statistical Database Management (SSDBM 2007)
6017:
5312:
Cluster analysis is widely used in market research when working with multivariate data from
4696:
4522:
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11266:
11054:
10629:
10578:
10554:
10516:
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10413:
10365:
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9493:
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8267:
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6888:
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Clustering is often utilized to locate and characterize extrema in the target distribution.
5234:
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2913:
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1045:
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6614:
18th International Conference on Scientific and Statistical Database Management (SSDBM'06)
6385:
1712:, and is commonly run multiple times with different random initializations. Variations of
8:
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10730:
10494:
10487:
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to use, a density threshold or the number of expected clusters) depend on the individual
998:
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621:
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497:
323:
256:
242:
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203:
153:
105:
65:
34:
The result of a cluster analysis shown as the coloring of the squares into three clusters
7932:
7230:
Proceedings of the 17th International Conference on Extending Database Technology (EDBT)
6892:
6825:
4413:
4386:
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8404:
7952:
7914:"Classifications of Atmospheric Circulation Patterns: Recent Advances and Applications"
7797:
7754:
7729:
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7487:
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6964:
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6759:
6719:
6700:
6645:
6558:
6507:
6485:
6467:
6422:
6398:
6358:
6214:; Sander, Jörg (1999). "OPTICS: Ordering Points To Identify the Clustering Structure".
6211:
6178:
6140:
6112:
6094:
5905:
5683:
5435:
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5409:
5348:
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4656:
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4443:
3649:
3625:
2993:
2973:
2953:
2529:
2482:
2203:
2193:
2143:
1867:). Here, the data set is usually modeled with a fixed (to avoid overfitting) number of
1679:
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1273:
1076:
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983:
663:
587:
373:
168:
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7606:
7414:. European Chapter of the Association for Computational Linguistics. pp. 345â355.
3759:{\displaystyle F_{\beta }={\frac {(\beta ^{2}+1)\cdot P\cdot R}{\beta ^{2}\cdot P+R}}}
2943:
As with internal evaluation, several external evaluation measures exist, for example:
2238:" evaluation by evaluating the utility of the clustering in its intended application.
2051:
assumes clusters of similar density, and may have problems separating nearby clusters.
11210:
11121:
11091:
11083:
10903:
10894:
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7514:
7369:
7166:
7106:
7066:
7038:
7028:
7002:
6992:
6914:
6879:
Frey, B. J.; Dueck, D. (2007). "Clustering by Passing Messages Between Data Points".
6790:
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6690:
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6537:
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5996:
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Cluster analysis refers to a family of algorithms and tasks rather than one specific
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The similarity of genetic data is used in clustering to infer population structures.
4573:
is an external validation index that measure the clustering results by applying the
1878:
Distribution-based clustering produces complex models for clusters that can capture
11146:
11101:
10865:
10852:
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10720:
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7506:
7483:
7446:
7438:
7396:
7392:
7357:
7311:
7307:
7272:
7251:
Rand, W. M. (1971). "Objective criteria for the evaluation of clustering methods".
7233:
7228:; Sander, J.; Goebel, R. (2014). "Model Selection for Semi-Supervised Clustering".
7162:
7135:
6968:
6956:
6926:
6906:
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6763:
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6704:
6682:
6649:
6627:
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6459:
6426:
6414:
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6261:
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6086:
6059:
6032:
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5953:
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5402:
5281:
5238:
4598:
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2207:
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1937:
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1022:
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784:
537:
487:
397:
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213:
208:
148:
46:
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and test panels. Market researchers use cluster analysis to partition the general
11063:
10807:
10669:
10596:
10271:
10145:
10118:
10095:
10064:
9691:
9686:
9640:
9370:
9021:
8750:
8694:
8516:
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8078:
6786:
6584:
6164:
5926:
5307:
5271:
5177:
4407:
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3304:
3277:
1988:
parameter entirely and offering performance improvements over OPTICS by using an
1847:
While the theoretical foundation of these methods is excellent, they suffer from
1764:
1631:) are known: SLINK for single-linkage and CLINK for complete-linkage clustering.
1123:
812:
616:
482:
422:
10553:
2902:-means clustering, and is also used to determine the optimal number of clusters.
1036:
between cluster members, dense areas of the data space, intervals or particular
11012:
11007:
9470:
9400:
9046:
8724:
8689:
8679:
8504:
8262:
8088:
7970:
Arnott, Robert D. (1980-11-01). "Cluster Analysis and Stock Price Comovement".
7510:
7237:
6777:
MeilÄ, Marina (2003). "Comparing Clusterings by the Variation of Information".
5922:
5509:
5367:
5356:
5230:
5203:
3919:
The Jaccard index is used to quantify the similarity between two datasets. The
3466:
3445:
3441:
1396:): objects may belong to more than one cluster; usually involving hard clusters
1299:
1277:
1085:
1010:
1002:
832:
363:
100:
7881:"Determining Structural Similarity of Chemicals Using Graph Theoretic Indices"
7442:
7139:
6960:
6463:
6354:
6308:
6063:
4786:
uniformly randomly distributed data points. Now define two distance measures,
4549:. Chance normalized versions of recall, precision and G-measure correspond to
11255:
11169:
11136:
10999:
10960:
10771:
10740:
10204:
10158:
9763:
9465:
9292:
9056:
9051:
8669:
8649:
8566:
8245:
7991:
7860:
Bewley, A.; et al. "Real-time volume estimation of a dragline payload".
7696:
7649:
7614:
7563:
7061:
Manning, Christopher D.; Raghavan, Prabhakar; SchĂŒtze, Hinrich (2008-07-07).
7042:
7006:
6090:
6036:
5977:
5818:
5566:
To find weather regimes or preferred sea level pressure atmospheric patterns.
4353:
3250:
2933:
2226:" evaluation, where the clustering is summarized to a single quality score, "
2134:. With the recent need to process larger and larger data sets (also known as
1872:
1709:
994:
751:
680:
562:
293:
178:
7940:
7672:
7503:
2004 IEEE International Conference on Fuzzy Systems (IEEE Cat. No.04CH37542)
6989:
The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data
6910:
6686:
6018:"SLINK: an optimally efficient algorithm for the single-link cluster method"
4044:{\displaystyle J(A,B)={\frac {|A\cap B|}{|A\cup B|}}={\frac {TP}{TP+FP+FN}}}
2173:
of their attributes. Examples for such clustering algorithms are CLIQUE and
11111:
11044:
11021:
10936:
10266:
9562:
9460:
9395:
9337:
9322:
9259:
9214:
8755:
8586:
8001:
7948:
7763:
7714:
7657:
7641:
7225:
7188:
6918:
6723:
6664:
6609:
6562:
6511:
6402:
6116:
5832:
5773:
University of California Publications in American Archaeology and Ethnology
5424:
5280:, cluster analysis can be used to differentiate between different types of
4550:
1375:: objects can also belong to no cluster; in which case they are considered
1269:
1220:
1119:
1115:
7983:
7571:
7427:"External clustering validity index based on chi-squared statistical test"
7023:
Weiss, Sholom M.; Indurkhya, Nitin; Zhang, Tong; Damerau, Fred J. (2005).
6745:
6631:
6341:-means algorithm for clustering large data sets with categorical values".
5901:
3879:
allocates an increasing amount of weight to recall in the final F-measure.
1445:
1406:: objects that belong to a child cluster also belong to the parent cluster
11154:
11116:
10799:
10700:
10562:
10375:
10342:
9834:
9751:
9746:
9390:
9347:
9327:
9307:
9297:
9066:
8851:
8622:
8531:
8526:
8148:
8126:
7793:
7199:
7126:
Dunn, J. (1974). "Well separated clusters and optimal fuzzy partitions".
5561:
5371:
5186:
with related expression patterns (also known as coexpressed genes) as in
5167:
4906:
from its nearest neighbor in X. We then define the Hopkins statistic as:
4318:{\displaystyle FM={\sqrt {{\frac {TP}{TP+FP}}\cdot {\frac {TP}{TP+FN}}}}}
3511:(both external evaluation measures in themselves) be defined as follows:
2273:-means, nor of an evaluation criterion that assumes convexity, is sound.
2170:
1848:
1735:
557:
51:
7501:
Banerjee, A. (2004). "Validating clusters using the Hopkins statistic".
7451:
6533:
6388:." In: Proc. Int'l Conf. on Management of Data, ACM SIGMOD, pp. 103â114.
6265:
6050:
Defays, D. (1977). "An efficient algorithm for a complete link method".
2066:
is a DBSCAN variant, improving handling of different densities clusters.
10000:
9480:
9180:
9111:
9061:
9036:
8956:
8745:
8704:
8699:
8612:
8521:
8429:
8341:
8321:
7826:"2022 Accelerate State of DevOps Report". 29 September 2022: 8, 14, 74.
7555:
7361:
7319:
7284:
6781:. Lecture Notes in Computer Science. Vol. 2777. pp. 173â187.
6569:. Lecture Notes in Computer Science. Vol. 4443. pp. 152â163.
6565:(2007). "Detection and Visualization of Subspace Cluster Hierarchies".
6518:. Lecture Notes in Computer Science. Vol. 4213. pp. 446â453.
6250:. Lecture Notes in Computer Science. Vol. 3918. pp. 119â128.
6186:
5968:
5317:
5114: in this section. Unsourced material may be challenged and removed.
4554:
3437:
3107:
2686:
2092:
Randomly select a cell âcâ, where c should not be traversed beforehand.
2011:
1463:
1060:
and model parameters until the result achieves the desired properties.
991:
706:
402:
328:
7688:
7025:
Text Mining: Predictive Methods for Analyzing Unstructured Information
6816:
6726:(2007). "On Exploring Complex Relationships of Correlation Clusters".
6418:
5491:
Clustering has been used to analyse the effectiveness of DevOps teams.
1914:
Density-based clusters cannot be modeled using Gaussian distributions.
10153:
10005:
9625:
9420:
9332:
9317:
9312:
9277:
8740:
8709:
8607:
8451:
8414:
8351:
8305:
8300:
8285:
7542:
Johnson, Stephen C. (1967-09-01). "Hierarchical clustering schemes".
6219:
5863:
5810:
5384:. There are currently a number of web-based clustering tools such as
5325:
5171:
4693:
dimensional space. Consider a random sample (without replacement) of
4481:
3670:
rate. We can calculate the F-measure by using the following formula:
3457:
3089:{\displaystyle {\frac {1}{N}}\sum _{m\in M}\max _{d\in D}{|m\cap d|}}
2868:
2082:
1717:
1276:
from the product of the signs on the edges. Under the assumptions of
1029:
865:
646:
7276:
7204:
MultiClust: Discovering, Summarizing, and Using Multiple Clusterings
6809:
6407:
Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
6136:
5590:
The clustering of chemical properties in different sample locations.
5198:, or genes that are co-regulated. High throughput experiments using
5142:
5089:
1863:
One prominent method is known as Gaussian mixture models (using the
9669:
9287:
9164:
9159:
9154:
8642:
8474:
7728:
Filipovych, Roman; Resnick, Susan M.; Davatzikos, Christos (2011).
7267:
5797:
Zubin, Joseph (1938). "A technique for measuring like-mindedness".
5321:
5277:
5264:
5245:
5211:
5190:. Often such groups contain functionally related proteins, such as
3905:
is not taken into account and can vary from 0 upward without bound.
2476:
2135:
1049:
7337:. International Conference on Cognitive Science. pp. 529â534.
7187:; Kröger, Peer; MĂŒller, Emmanuel; Schubert, Erich; Seidl, Thomas;
11174:
10875:
8765:
8602:
8556:
8479:
8379:
8374:
8326:
7673:"High-Throughput Genotyping with Single Nucleotide Polymorphisms"
6862:"Clustering by a Genetic Algorithm with Biased Mutation Operator"
5251:
Clustering algorithms are used to automatically assign genotypes.
5161:
2214:, has led to the creation of new types of clustering algorithms.
1693:
641:
1181:: clusters are modeled using statistical distributions, such as
11096:
10077:
10051:
10031:
9282:
9073:
8780:
8760:
8632:
8424:
7210:
6667:(2004). "Computing Clusters of Correlation Connected objects".
6490:
Density-Connected Subspace Clustering for High-Dimensional Data
5486:
5394:
5385:
5381:
5191:
5158:
2246:
does not exist a different, and maybe even better, clustering.
2185:
2181:
2180:
Ideas from density-based clustering methods (in particular the
2174:
2048:
2034:
2000:
1996:
1989:
1929:
1836:
The clustering framework most closely related to statistics is
1724:
1685:
gives a formal definition as an optimization problem: find the
1205:
1201:
392:
7879:
Basak, S.C.; Magnuson, V.R.; Niemi, C.J.; Regal, R.R. (1988).
7223:
6439:
5574:
Cluster analysis has been used to cluster stocks into sectors.
1208:
defines clusters as connected dense regions in the data space.
1067:, there is a number of terms with similar meanings, including
8925:
8581:
8561:
8551:
8546:
8541:
8536:
8499:
8331:
5427:
5183:
5154:
3307:. The instances being counted here are the number of correct
1490:
1486:
636:
631:
358:
7182:
6386:
An Efficient Data Clustering Method for Very Large Databases
30:
9016:
8571:
7727:
6209:
1902:
works well, since it uses Gaussians for modelling clusters.
7022:
6158:
Microsoft academic search: most cited data mining articles
5925:(May 1967) "Clustering and structural balance in graphs",
6567:
Advances in Databases: Concepts, Systems and Applications
6216:
ACM SIGMOD international conference on Management of data
5284:
in a three-dimensional image for many different purposes.
3465:
The F-measure can be used to balance the contribution of
2680:
is considered the best algorithm based on this criterion.
1354:
There are also finer distinctions possible, for example:
7862:
IEEE International Conference on Robotics and Automation
7091:
Knowledge Discovery in Databases â Part III â Clustering
6946:
6717:
6556:
6505:
6111:
5599:
4484:, while the F-measure is their harmonic mean. Moreover,
2112:
Repeat steps 2,3 and 4 till all the cells are traversed.
1964:. DeLi-Clu, Density-Link-Clustering combines ideas from
1960:, and produces a hierarchical result related to that of
1824:-medoids are special cases of the uncapacitated, metric
924:
List of datasets in computer vision and image processing
7878:
7775:
7773:
6077:
Lloyd, S. (1982). "Least squares quantization in PCM".
2871:
of the clusters. Similarly, the intra-cluster distance
1446:
Connectivity-based clustering (hierarchical clustering)
7670:
7060:
6662:
6612:(2006). "Mining Hierarchies of Correlation Clusters".
6607:
5661:
1856:
methods include more parsimonious models based on the
4914:
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4852:
4819:
4792:
4772:
4752:
4725:
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4679:
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4446:
4416:
4389:
4362:
4335:
4240:
4149:
4124:
4101:
4064:
3932:
3888:
3865:
3839:
3802:
3776:
3678:
3652:
3628:
3572:
3519:
3479:
3363:
3340:
3317:
3286:
3259:
3232:
3209:
3123:
3022:
2996:
2976:
2956:
2703:
2655:
2628:
2579:
2552:
2532:
2505:
2485:
2454:
2302:
2098:
If the density of âcâ greater than threshold density
1974:
1946:
1597:
1547:
1507:
1126:
beginning in 1943 for trait theory classification in
10838:
Autoregressive conditional heteroskedasticity (ARCH)
7770:
7224:
Pourrajabi, M.; Moulavi, D.; Campello, R. J. G. B.;
7193:"On Using Class-Labels in Evaluation of Clusterings"
6514:(2006). "Finding Hierarchies of Subspace Clusters".
6176:
5694:
2008:
that are able to precisely model this kind of data.
1928:
The most popular density-based clustering method is
7819:
7627:
6167:: DBSCAN is on rank 24, when accessed on: 4/18/2010
5769:"Quantitative Expression of Cultural Relationships"
2526:is the average distance of all elements in cluster
10300:
7730:"Semi-supervised Cluster Analysis of Imaging Data"
7261:(336). American Statistical Association: 846â850.
7178:
7176:
7152:
6397:
6291:Data Clustering : Algorithms and Applications
5766:
5052:
4898:
4865:
4838:
4805:
4778:
4758:
4738:
4711:
4685:
4665:
4645:
4541:
4511:
4472:
4452:
4425:
4398:
4371:
4344:
4317:
4208:
4133:
4110:
4073:
4043:
3897:
3871:
3851:
3821:
3788:
3758:
3658:
3634:
3610:
3557:
3491:
3424:
3349:
3326:
3295:
3268:
3241:
3218:
3191:
3088:
3002:
2982:
2962:
2811:
2668:
2641:
2614:
2565:
2538:
2518:
2491:
2467:
2432:
1980:
1952:
1623:
1579:
1533:
6245:
5143:Biology, computational biology and bioinformatics
4095:The Dice symmetric measure doubles the weight on
3416:
3403:
2851:) measures the intra-cluster distance of cluster
2104:Calculate the density of all the neighbors of âcâ
11253:
7155:Journal of Computational and Applied Mathematics
5938:
5733:Determining the number of clusters in a data set
3050:
2762:
2714:
2344:
2260:Determining the number of clusters in a data set
2089:Divide data space into a finite number of cells.
1899:
1170:represents each cluster by a single mean vector.
10386:Multivariate adaptive regression splines (MARS)
7779:
7385:Journal of the American Statistical Association
7300:Journal of the American Statistical Association
7254:Journal of the American Statistical Association
7173:
6248:Advances in Knowledge Discovery and Data Mining
5887:
3192:{\displaystyle RI={\frac {TP+TN}{TP+FP+FN+TN}}}
2159:clustering algorithms for high-dimensional data
2153:, many of the existing methods fail due to the
1811:-means cannot represent density-based clusters.
1734:), choosing the initial centers less randomly (
1692:The optimization problem itself is known to be
7473:
7297:
6384:Tian Zhang, Raghu Ramakrishnan, Miron Livny. "
6289:Aggarwal, Charu C.; Reddy, Chandan K. (eds.).
2192:Several different clustering systems based on
1040:. Clustering can therefore be formulated as a
919:List of datasets for machine-learning research
8941:
8017:
6986:
6494:Proc. SIAM Int. Conf. on Data Mining (SDM'04)
6241:
6239:
5799:The Journal of Abnormal and Social Psychology
1456:Connectivity-based clustering, also known as
1155:builds models based on distance connectivity.
952:
8031:
7592:
6987:Feldman, Ronen; Sanger, James (2007-01-01).
4209:{\displaystyle DSC={\frac {2TP}{2TP+FP+FN}}}
2294:can be calculated by the following formula:
2217:
1663:
1371:Strict partitioning clustering with outliers
1365:: each object belongs to exactly one cluster
7348:Arabie, P. (1985). "Comparing partitions".
6942:
6940:
6938:
6936:
6516:Knowledge Discovery in Databases: PKDD 2006
5225:is used to group homologous sequences into
3425:{\displaystyle TP+TN+FP+FN={\binom {N}{2}}}
2835:) represents the distance between clusters
1919:
1816:Centroid-based clustering problems such as
8986:
8948:
8934:
8024:
8010:
7217:
7111:: CS1 maint: location missing publisher (
6236:
5883:
5881:
5879:
5877:
5875:
5873:
2196:have been proposed. One is Marina MeilÄ's
1887:Gaussian mixture model clustering examples
1741:) or allowing a fuzzy cluster assignment (
1501:). In the general case, the complexity is
959:
945:
9599:
7896:
7808:
7753:
7704:
7450:
7335:Recall and Precision versus the Bookmaker
7266:
6900:
6878:
6815:
6735:
6676:
6621:
6574:
6523:
6453:
6288:
6255:
6227:
6125:WIREs Data Mining and Knowledge Discovery
5967:
5957:
5852:Journal of Abnormal and Social Psychology
5301:
5130:Learn how and when to remove this message
5046:
2805:
1831:
1330:: each object belongs to a cluster or not
7500:
6933:
6859:
6058:(4). British Computer Society: 364â366.
2071:
1466:, which explains where the common name "
29:
7541:
7382:
6321:
6079:IEEE Transactions on Information Theory
5995:. Chichester, West Sussex, U.K: Wiley.
5990:
5870:
5849:
4629:There are multiple formulations of the
2076:The grid-based technique is used for a
1580:{\displaystyle {\mathcal {O}}(2^{n-1})}
1485:(the maximum of object distances), and
27:Grouping a set of objects by similarity
14:
11254:
10912:KaplanâMeier estimator (product limit)
7969:
7409:
7347:
7332:
7326:
6049:
6031:(1). British Computer Society: 30â34.
6015:
5370:, clustering may be used to recognize
5229:. This is a very important concept in
5210: – a general aspect of
5182:Clustering is used to build groups of
3010:data points, purity can be defined as:
2906:
2253:
2119:
1706:another algorithm introduced this name
10985:
10552:
10299:
9598:
9368:
8985:
8929:
8005:
7403:
7198:. In Fern, Xiaoli Z.; Davidson, Ian;
7084:
7082:
7063:Introduction to Information Retrieval
7018:
7016:
6982:
6980:
6978:
6776:
6337:Huang, Z. (1998). "Extensions to the
6336:
6210:Ankerst, Mihael; Breunig, Markus M.;
6076:
6070:
5831:
5796:
5600:Specialized types of cluster analysis
3611:{\displaystyle R={\frac {TP}{TP+FN}}}
3558:{\displaystyle P={\frac {TP}{TP+FP}}}
2234:" evaluation by a human expert, and "
1624:{\displaystyle {\mathcal {O}}(n^{2})}
1534:{\displaystyle {\mathcal {O}}(n^{3})}
11222:
10922:Accelerated failure time (AFT) model
8862:Generative adversarial network (GAN)
7911:
7250:
7125:
6405:(July 2012). "Subspace clustering".
5502:Sequence analysis in social sciences
5112:adding citations to reliable sources
5083:
4492:are also known as Wallace's indices
3833:has no impact on the F-measure when
11234:
10517:Analysis of variance (ANOVA, anova)
9369:
6779:Learning Theory and Kernel Machines
6663:Böhm, C.; Kailing, K.; Kröger, P.;
6608:Achtert, E.; Böhm, C.; Kröger, P.;
6442:Data Mining and Knowledge Discovery
6343:Data Mining and Knowledge Discovery
5662:Techniques used in cluster analysis
5423:Clustering can be used to divide a
5401:
4846:from its nearest neighbor in X and
4633:. A typical one is as follows. Let
4610:
4433:index is the geometric mean of the
1752:-means-type algorithms require the
1481:(the minimum of object distances),
914:Glossary of artificial intelligence
24:
10612:CochranâMantelâHaenszel statistics
9238:Pearson product-moment correlation
7859:
7488:10.1093/oxfordjournals.aob.a083391
7383:Wallace, D. L. (1983). "Comment".
7183:FĂ€rber, Ines; GĂŒnnemann, Stephan;
7079:
7013:
6975:
5890:ACM SIGKDD Explorations Newsletter
5479:Clustering can be used to resolve
5287:Analysis of antimicrobial activity
3407:
2788:
2622:is the distance between centroids
1865:expectation-maximization algorithm
1773:Expectation-maximization algorithm
1600:
1550:
1510:
1249:, that is, a subset of nodes in a
1187:expectation-maximization algorithm
25:
11283:
7505:. Vol. 1. pp. 149â153.
6949:Knowledge and Information Systems
6860:Auffarth, B. (July 18â23, 2010).
6561:; Kröger, P.; MĂŒller-Gorman, I.;
6510:; Kröger, P.; MĂŒller-Gorman, I.;
5695:Data projection and preprocessing
5679:Neighbourhood components analysis
5495:
5355:
3226:is the number of true positives,
2022:Density-based clustering examples
1541:for agglomerative clustering and
1183:multivariate normal distributions
997:, used in many fields, including
11233:
11221:
11209:
11196:
11195:
10986:
8900:
8899:
8879:
7746:10.1016/j.neuroimage.2010.09.074
7482:(2). Annals Botany Co: 213â227.
5616:Clustering high-dimensional data
5459:Markov chain Monte Carlo methods
5088:
4480:, and is thus also known as the
2056:
2041:
2026:
1907:
1891:
1801:
1786:
1775:for this model discussed below.
1651:
1639:
1497:phenomenon", in particular with
10871:Least-squares spectral analysis
7963:
7905:
7872:
7853:
7721:
7664:
7621:
7586:
7535:
7494:
7467:
7418:
7376:
7341:
7291:
7244:
7146:
7119:
6872:
6853:
6803:
6770:
6711:
6656:
6601:
6550:
6499:
6478:
6433:
6391:
6378:
6369:
6330:
6315:
6282:
6203:
6170:
6151:
6105:
6043:
5606:Automatic clustering algorithms
5099:needs additional citations for
5079:
1708:). It does however only find a
9852:Mean-unbiased minimum-variance
8955:
8812:Recurrent neural network (RNN)
8802:Differentiable neural computer
7912:Huth, R.; et al. (2008).
7595:Information Processing Letters
7397:10.1080/01621459.1983.10478009
7312:10.1080/01621459.1983.10478008
7065:. Cambridge University Press.
6115:; Kröger, Peer; Sander, Jörg;
6009:
5984:
5959:10.1016/j.psychres.2023.115265
5932:
5916:
5843:
5825:
5790:
5760:
5374:within large groups of people.
5149:Distance matrices in phylogeny
3993:
3979:
3972:
3958:
3948:
3936:
3714:
3695:
3081:
3067:
2799:
2793:
2756:
2744:
2615:{\displaystyle d(c_{i},c_{j})}
2609:
2583:
2420:
2394:
2101:Mark cell âcâ as a new cluster
2033:Density-based clustering with
1898:On Gaussian-distributed data,
1618:
1605:
1574:
1555:
1528:
1515:
1361:Strict partitioning clustering
1312:Independent Component Analysis
334:Relevance vector machine (RVM)
13:
1:
11165:Geographic information system
10381:Simultaneous equations models
8857:Variational autoencoder (VAE)
8817:Long short-term memory (LSTM)
8084:Computational learning theory
7607:10.1016/S0020-0190(00)00142-3
5753:
5237:in general. See evolution by
4592:Normalized mutual information
2855:. The inter-cluster distance
1700:, often just referred to as "
1436:list of statistics algorithms
1422:
1133:
1122:in 1939 and famously used by
990:, and a common technique for
823:Computational learning theory
387:Expectationâmaximization (EM)
10348:Coefficient of determination
9959:Uniformly most powerful test
8837:Convolutional neural network
7898:10.1016/0166-218x(88)90004-2
7630:Journal of Molecular Biology
7167:10.1016/0377-0427(87)90125-7
6787:10.1007/978-3-540-45167-9_14
6585:10.1007/978-3-540-71703-4_15
6324:Web-scale k-means clustering
5706:Principal component analysis
3492:{\displaystyle \beta \geq 0}
2095:Calculate the density of âcâ
1981:{\displaystyle \varepsilon }
1968:and OPTICS, eliminating the
1953:{\displaystyle \varepsilon }
1308:Principal Component Analysis
1042:multi-objective optimization
780:Coefficient of determination
627:Convolutional neural network
339:Support vector machine (SVM)
7:
10917:Proportional hazards models
10861:Spectral density estimation
10843:Vector autoregression (VAR)
10277:Maximum posterior estimator
9509:Randomized controlled trial
8832:Multilayer perceptron (MLP)
5767:Driver and Kroeber (1932).
5594:
5475:Natural language processing
5340:and selecting test markets.
5263:
5206:can be a powerful tool for
2519:{\displaystyle \sigma _{i}}
2448:is the number of clusters,
1635:Linkage clustering examples
1483:complete linkage clustering
1429:Cluster analysis algorithms
931:Outline of machine learning
828:Empirical risk minimization
10:
11288:
10677:Multivariate distributions
9097:Average absolute deviation
8908:Artificial neural networks
8822:Gated recurrent unit (GRU)
8048:Differentiable programming
7972:Financial Analysts Journal
7511:10.1109/FUZZY.2004.1375706
6121:"Density-based Clustering"
5430:into distinct regions for
5343:Grouping of shopping items
5146:
4899:{\displaystyle x_{i}\in X}
4839:{\displaystyle y_{i}\in Y}
4081:is not taken into account.
2257:
2206:, a recent development in
1880:correlation and dependence
1782:-means clustering examples
1667:
1449:
1426:
568:Feedforward neural network
319:Artificial neural networks
11191:
11145:
11082:
11035:
10998:
10994:
10981:
10953:
10935:
10902:
10893:
10851:
10798:
10759:
10708:
10699:
10665:Structural equation model
10620:
10577:
10573:
10548:
10507:
10473:
10427:
10394:
10356:
10323:
10319:
10295:
10235:
10144:
10063:
10027:
10018:
10001:Score/Lagrange multiplier
9986:
9939:
9884:
9810:
9801:
9611:
9607:
9594:
9553:
9527:
9479:
9434:
9416:Sample size determination
9381:
9377:
9364:
9268:
9223:
9197:
9179:
9135:
9087:
9007:
8998:
8994:
8981:
8963:
8875:
8789:
8733:
8662:
8595:
8467:
8367:
8360:
8314:
8278:
8241:Artificial neural network
8221:
8097:
8064:Automatic differentiation
8037:
7782:Computational Linguistics
7443:10.1016/j.ins.2019.02.046
7350:Journal of Classification
7140:10.1080/01969727408546059
6991:. Cambridge Univ. Press.
6961:10.1007/s10115-008-0150-6
6464:10.1007/s10618-005-1396-1
5723:Cluster-weighted modeling
5668:Artificial neural network
5533:
4719:data points with members
2218:Evaluation and assessment
2016:kernel density estimation
1966:single-linkage clustering
1826:facility location problem
1664:Centroid-based clustering
1499:single-linkage clustering
1479:single-linkage clustering
1089:
1038:statistical distributions
988:exploratory data analysis
551:Artificial neural network
11160:Environmental statistics
10682:Elliptical distributions
10475:Generalized linear model
10404:Simple linear regression
10174:HodgesâLehmann estimator
9631:Probability distribution
9540:Stochastic approximation
9102:Coefficient of variation
8069:Neuromorphic engineering
8032:Differentiable computing
7238:10.5441/002/edbt.2014.31
6955:(3). Springer: 361â394.
6091:10.1109/TIT.1982.1056489
5743:Structured data analysis
5716:
5711:Multidimensional scaling
5255:Human genetic clustering
5188:HCS clustering algorithm
3852:{\displaystyle \beta =0}
3789:{\displaystyle \beta =0}
2970:and some set of classes
2199:variation of information
1920:Density-based clustering
1858:eigenvalue decomposition
1256:HCS clustering algorithm
860:Journals and conferences
807:Mathematical foundations
717:Temporal difference (TD)
573:Recurrent neural network
493:Conditional random field
416:Dimensionality reduction
164:Dimensionality reduction
126:Quantum machine learning
121:Neuromorphic engineering
81:Self-supervised learning
76:Semi-supervised learning
10820:Cross-correlation (XCF)
10428:Non-standard predictors
9862:LehmannâScheffĂ© theorem
9535:Adaptive clinical trial
8842:Residual neural network
8258:Artificial Intelligence
7941:10.1196/annals.1446.019
6911:10.1126/science.1136800
6718:Achtert, E.; Bohm, C.;
6687:10.1145/1007568.1007620
6557:Achtert, E.; Böhm, C.;
6506:Achtert, E.; Böhm, C.;
6355:10.1023/A:1009769707641
6064:10.1093/comjnl/20.4.364
5991:Everitt, Brian (2011).
5728:Curse of dimensionality
5674:Nearest neighbor search
5519:Educational data mining
5442:Evolutionary algorithms
5391:Slippy map optimization
5363:Social network analysis
5338:new product development
5200:expressed sequence tags
5170:to generate artificial
4561:and relate strongly to
3822:{\displaystyle F_{0}=P}
2921:. Additionally, from a
2155:curse of dimensionality
1468:hierarchical clustering
1459:hierarchical clustering
1452:Hierarchical clustering
1402:Hierarchical clustering
1153:hierarchical clustering
269:Apprenticeship learning
11216:Mathematics portal
11037:Engineering statistics
10945:NelsonâAalen estimator
10522:Analysis of covariance
10409:Ordinary least squares
10333:Pearson product-moment
9737:Statistical functional
9648:Empirical distribution
9481:Controlled experiments
9210:Frequency distribution
8988:Descriptive statistics
7839:Cite journal requires
7642:10.1006/jmbi.2000.5197
7412:The Problem with Kappa
7410:Powers, David (2012).
7333:Powers, David (2003).
7128:Journal of Cybernetics
6841:Cite journal requires
6037:10.1093/comjnl/16.1.30
5641:Data stream clustering
5631:Constrained clustering
5548:Mathematical chemistry
5377:Search result grouping
5302:Business and marketing
5054:
5025:
4984:
4944:
4900:
4873:to be the distance of
4867:
4840:
4813:to be the distance of
4807:
4780:
4760:
4746:. Also generate a set
4740:
4713:
4712:{\displaystyle m\ll n}
4687:
4667:
4647:
4543:
4542:{\displaystyle B^{II}}
4513:
4474:
4454:
4427:
4400:
4373:
4346:
4319:
4210:
4135:
4112:
4075:
4045:
3899:
3873:
3872:{\displaystyle \beta }
3853:
3823:
3790:
3760:
3660:
3636:
3612:
3559:
3493:
3426:
3351:
3328:
3311:assignments. That is,
3297:
3270:
3243:
3220:
3193:
3090:
3004:
2984:
2964:
2927:constrained clustering
2890:Silhouette coefficient
2813:
2670:
2643:
2616:
2567:
2540:
2520:
2493:
2469:
2434:
2342:
2167:correlation clustering
1982:
1954:
1869:Gaussian distributions
1854:model-based clustering
1838:model-based clustering
1832:Model-based clustering
1625:
1581:
1535:
1390:alternative clustering
1384:Overlapping clustering
1295:: the most well-known
1128:personality psychology
818:Biasâvariance tradeoff
700:Reinforcement learning
676:Spiking neural network
86:Reinforcement learning
35:
11132:Population statistics
11074:System identification
10808:Autocorrelation (ACF)
10736:Exponential smoothing
10650:Discriminant analysis
10645:Canonical correlation
10509:Partition of variance
10371:Regression validation
10215:(JonckheereâTerpstra)
10114:Likelihood-ratio test
9803:Frequentist inference
9715:Locationâscale family
9636:Sampling distribution
9601:Statistical inference
9568:Cross-sectional study
9555:Observational studies
9514:Randomized experiment
9343:Stem-and-leaf display
9145:Central limit theorem
8797:Neural Turing machine
8385:Human image synthesis
7984:10.2469/faj.v36.n6.56
7099:Heidelberg University
6746:10.1109/SSDBM.2007.21
6632:10.1109/SSDBM.2006.35
5902:10.1145/568574.568575
5684:Latent class analysis
5621:Conceptual clustering
5055:
5005:
4964:
4924:
4901:
4868:
4866:{\displaystyle w_{i}}
4841:
4808:
4806:{\displaystyle u_{i}}
4781:
4761:
4741:
4739:{\displaystyle x_{i}}
4714:
4688:
4668:
4648:
4588:information theoretic
4575:chi-squared statistic
4544:
4514:
4512:{\displaystyle B^{I}}
4475:
4455:
4428:
4401:
4374:
4347:
4320:
4224:FowlkesâMallows index
4211:
4136:
4118:while still ignoring
4113:
4076:
4046:
3900:
3874:
3854:
3824:
3791:
3761:
3661:
3637:
3613:
3560:
3494:
3427:
3352:
3329:
3298:
3271:
3244:
3221:
3194:
3091:
3005:
2985:
2965:
2814:
2671:
2669:{\displaystyle c_{j}}
2644:
2642:{\displaystyle c_{i}}
2617:
2568:
2566:{\displaystyle c_{i}}
2541:
2521:
2494:
2470:
2468:{\displaystyle c_{i}}
2435:
2322:
2151:high-dimensional data
2072:Grid-based clustering
1983:
1955:
1626:
1582:
1536:
1394:multi-view clustering
1007:information retrieval
654:Neural radiance field
476:Structured prediction
199:Structured prediction
71:Unsupervised learning
33:
11055:Probabilistic design
10640:Principal components
10483:Exponential families
10435:Nonlinear regression
10414:General linear model
10376:Mixed effects models
10366:Errors and residuals
10343:Confounding variable
10245:Bayesian probability
10223:Van der Waerden test
10213:Ordered alternative
9978:Multiple comparisons
9857:RaoâBlackwellization
9820:Estimating equations
9776:Statistical distance
9494:Factorial experiment
9027:Arithmetic-Geometric
8888:Computer programming
8867:Graph neural network
8442:Text-to-video models
8420:Text-to-image models
8268:Large language model
8253:Scientific computing
8059:Statistical manifold
8054:Information geometry
7794:10.1162/COLI_a_00148
7431:Information Sciences
7232:. pp. 331â342.
6616:. pp. 119â128.
6496:, pp. 246â257, 2004.
6322:Sculley, D. (2010).
6189:. pp. 226â231.
6052:The Computer Journal
6025:The Computer Journal
5738:Parallel coordinates
5689:Affinity propagation
5626:Consensus clustering
5235:evolutionary biology
5108:improve this article
4912:
4877:
4850:
4817:
4790:
4770:
4750:
4723:
4697:
4677:
4657:
4637:
4559:Matthews Correlation
4523:
4496:
4464:
4444:
4414:
4387:
4360:
4333:
4238:
4147:
4122:
4099:
4062:
3930:
3886:
3863:
3837:
3800:
3774:
3676:
3650:
3626:
3570:
3517:
3477:
3473:through a parameter
3361:
3338:
3315:
3284:
3257:
3230:
3207:
3121:
3020:
2994:
2990:, both partitioning
2974:
2954:
2701:
2678:DaviesâBouldin index
2653:
2626:
2577:
2550:
2530:
2503:
2483:
2452:
2300:
2292:DaviesâBouldin index
2283:DaviesâBouldin index
1995:The key drawback of
1972:
1944:
1840:, which is based on
1595:
1545:
1505:
1103:typological analysis
843:Statistical learning
741:Learning with humans
533:Local outlier factor
18:Cluster (statistics)
11127:Official statistics
11050:Methods engineering
10731:Seasonal adjustment
10499:Poisson regressions
10419:Bayesian regression
10358:Regression analysis
10338:Partial correlation
10310:Regression analysis
9909:Prediction interval
9904:Likelihood interval
9894:Confidence interval
9886:Interval estimation
9847:Unbiased estimators
9665:Model specification
9545:Up-and-down designs
9233:Partial correlation
9189:Index of dispersion
9107:Interquartile range
8234:In-context learning
8074:Pattern recognition
7933:2008NYASA1146..105H
7921:Ann. N.Y. Acad. Sci
7185:Kriegel, Hans-Peter
6893:2007Sci...315..972F
6826:2003q.bio....11039K
6534:10.1007/11871637_42
6399:Kriegel, Hans-Peter
6266:10.1007/11731139_16
6212:Kriegel, Hans-Peter
6179:Kriegel, Hans-Peter
6113:Kriegel, Hans-Peter
6016:Sibson, R. (1973).
5946:Psychiatry Research
5839:. Edwards Brothers.
5748:Linear separability
5701:Dimension reduction
5656:Spectral clustering
5651:Sequence clustering
5636:Community detection
5611:Balanced clustering
5554:topological indices
5450:Recommender systems
5334:product positioning
5330:market segmentation
5223:Sequence clustering
5041:
5000:
4960:
3450:adjusted Rand index
3436:One issue with the
2923:knowledge discovery
2907:External evaluation
2254:Internal evaluation
2212:statistical physics
2163:subspace clustering
2120:Recent developments
1842:distribution models
1732:-medians clustering
1589:divisive clustering
1413:Subspace clustering
1304:self-organizing map
1262:Signed graph models
1166:: for example, the
1108:community detection
1054:knowledge discovery
999:pattern recognition
686:Electrochemical RAM
593:reservoir computing
324:Logistic regression
243:Supervised learning
229:Multimodal learning
204:Feature engineering
149:Generative modeling
111:Rule-based learning
106:Curriculum learning
66:Supervised learning
41:Part of a series on
11147:Spatial statistics
11027:Medical statistics
10927:First hitting time
10881:Whittle likelihood
10532:Degrees of freedom
10527:Multivariate ANOVA
10460:Heteroscedasticity
10272:Bayesian estimator
10237:Bayesian inference
10086:KolmogorovâSmirnov
9971:Randomization test
9941:Testing hypotheses
9914:Tolerance interval
9825:Maximum likelihood
9720:Exponential family
9653:Density estimation
9613:Statistical theory
9573:Natural experiment
9519:Scientific control
9436:Survey methodology
9122:Standard deviation
8827:Echo state network
8715:JĂŒrgen Schmidhuber
8410:Facial recognition
8405:Speech recognition
8315:Software libraries
7556:10.1007/BF02289588
7362:10.1007/BF01908075
6486:Hans-Peter Kriegel
6222:. pp. 49â60.
6163:2010-04-21 at the
5436:object recognition
5419:Image segmentation
5410:Software evolution
5071:distribution, not
5050:
5027:
4986:
4946:
4896:
4863:
4836:
4803:
4776:
4756:
4736:
4709:
4683:
4663:
4643:
4583:mutual information
4539:
4509:
4470:
4450:
4426:{\displaystyle FM}
4423:
4399:{\displaystyle FN}
4396:
4372:{\displaystyle FP}
4369:
4345:{\displaystyle TP}
4342:
4315:
4206:
4134:{\displaystyle TN}
4131:
4111:{\displaystyle TP}
4108:
4074:{\displaystyle TN}
4071:
4041:
3898:{\displaystyle TN}
3895:
3869:
3849:
3829:. In other words,
3819:
3786:
3756:
3656:
3632:
3608:
3555:
3489:
3422:
3350:{\displaystyle FP}
3347:
3327:{\displaystyle TP}
3324:
3296:{\displaystyle FN}
3293:
3269:{\displaystyle FP}
3266:
3242:{\displaystyle TN}
3239:
3219:{\displaystyle TP}
3216:
3189:
3086:
3064:
3048:
3000:
2980:
2960:
2809:
2782:
2740:
2666:
2639:
2612:
2563:
2536:
2516:
2489:
2465:
2430:
2358:
2204:belief propagation
2194:mutual information
2144:k-means clustering
1978:
1962:linkage clustering
1950:
1754:number of clusters
1670:k-means clustering
1621:
1577:
1531:
1475:distance functions
1176:Distribution model
1146:Connectivity model
1077:numerical taxonomy
1058:data preprocessing
254: •
169:Density estimation
36:
11249:
11248:
11187:
11186:
11183:
11182:
11122:National accounts
11092:Actuarial science
11084:Social statistics
10977:
10976:
10973:
10972:
10969:
10968:
10904:Survival function
10889:
10888:
10751:Granger causality
10592:Contingency table
10567:Survival analysis
10544:
10543:
10540:
10539:
10396:Linear regression
10291:
10290:
10287:
10286:
10262:Credible interval
10231:
10230:
10014:
10013:
9830:Method of moments
9699:Parametric family
9660:Statistical model
9590:
9589:
9586:
9585:
9504:Random assignment
9426:Statistical power
9360:
9359:
9356:
9355:
9205:Contingency table
9175:
9174:
9042:Generalized/power
8923:
8922:
8685:Stephen Grossberg
8658:
8657:
7885:Discr. Appl. Math
7689:10.1101/gr.157801
7520:978-0-7803-8353-1
7072:978-0-521-86571-5
6887:(5814): 972â976.
6796:978-3-540-40720-1
6755:978-0-7695-2868-7
6641:978-0-7695-2590-7
6594:978-3-540-71702-7
6543:978-3-540-45374-1
6488:and Peer Kröger.
6419:10.1002/widm.1057
6326:. Proc. 19th WWW.
6300:978-1-315-37351-5
6275:978-3-540-33206-0
5579:Petroleum geology
5481:lexical ambiguity
5467:Anomaly detection
5328:, and for use in
5294:IMRT segmentation
5218:Sequence analysis
5208:genome annotation
5168:plant systematics
5140:
5139:
5132:
5044:
4779:{\displaystyle m}
4759:{\displaystyle Y}
4686:{\displaystyle d}
4666:{\displaystyle n}
4646:{\displaystyle X}
4631:Hopkins statistic
4622:Hopkins statistic
4473:{\displaystyle R}
4453:{\displaystyle P}
4406:is the number of
4379:is the number of
4352:is the number of
4313:
4311:
4279:
4204:
4039:
3998:
3859:, and increasing
3754:
3659:{\displaystyle R}
3635:{\displaystyle P}
3606:
3553:
3414:
3303:is the number of
3276:is the number of
3249:is the number of
3187:
3049:
3033:
3031:
3003:{\displaystyle N}
2983:{\displaystyle D}
2963:{\displaystyle M}
2803:
2761:
2713:
2539:{\displaystyle i}
2492:{\displaystyle i}
2424:
2343:
2320:
2140:canopy clustering
2078:multi-dimensional
1702:k-means algorithm
1698:Lloyd's algorithm
1683:-means clustering
1240:Graph-based model
1168:k-means algorithm
1063:Besides the term
1046:distance function
1019:computer graphics
969:
968:
774:Model diagnostics
757:Human-in-the-loop
600:Boltzmann machine
513:Anomaly detection
309:Linear regression
224:Ontology learning
219:Grammar induction
194:Semantic analysis
189:Association rules
174:Anomaly detection
116:Neuro-symbolic AI
16:(Redirected from
11279:
11262:Cluster analysis
11237:
11236:
11225:
11224:
11214:
11213:
11199:
11198:
11102:Crime statistics
10996:
10995:
10983:
10982:
10900:
10899:
10866:Fourier analysis
10853:Frequency domain
10833:
10780:
10746:Structural break
10706:
10705:
10655:Cluster analysis
10602:Log-linear model
10575:
10574:
10550:
10549:
10491:
10465:Homoscedasticity
10321:
10320:
10297:
10296:
10216:
10208:
10200:
10199:(KruskalâWallis)
10184:
10169:
10124:Cross validation
10109:
10091:AndersonâDarling
10038:
10025:
10024:
9996:Likelihood-ratio
9988:Parametric tests
9966:Permutation test
9949:1- & 2-tails
9840:Minimum distance
9812:Point estimation
9808:
9807:
9759:Optimal decision
9710:
9609:
9608:
9596:
9595:
9578:Quasi-experiment
9528:Adaptive designs
9379:
9378:
9366:
9365:
9243:Rank correlation
9005:
9004:
8996:
8995:
8983:
8982:
8950:
8943:
8936:
8927:
8926:
8913:Machine learning
8903:
8902:
8883:
8638:Action selection
8628:Self-driving car
8435:Stable Diffusion
8400:Speech synthesis
8365:
8364:
8229:Machine learning
8105:Gradient descent
8026:
8019:
8012:
8003:
8002:
7996:
7995:
7967:
7961:
7960:
7918:
7909:
7903:
7902:
7900:
7876:
7870:
7869:
7857:
7851:
7848:
7842:
7837:
7835:
7827:
7823:
7817:
7812:
7806:
7805:
7777:
7768:
7767:
7757:
7740:(3): 2185â2197.
7725:
7719:
7718:
7708:
7683:(7): 1262â1268.
7668:
7662:
7661:
7636:(5): 1041â1052.
7625:
7619:
7618:
7590:
7584:
7583:
7539:
7533:
7532:
7498:
7492:
7491:
7476:Annals of Botany
7471:
7465:
7464:
7454:
7422:
7416:
7415:
7407:
7401:
7400:
7391:(383): 569â579.
7380:
7374:
7373:
7345:
7339:
7338:
7330:
7324:
7323:
7306:(383): 553â569.
7295:
7289:
7288:
7270:
7248:
7242:
7241:
7221:
7215:
7214:
7197:
7180:
7171:
7170:
7150:
7144:
7143:
7123:
7117:
7116:
7110:
7102:
7096:
7086:
7077:
7076:
7058:
7047:
7046:
7020:
7011:
7010:
6984:
6973:
6972:
6944:
6931:
6930:
6904:
6876:
6870:
6869:
6857:
6851:
6850:
6844:
6839:
6837:
6829:
6819:
6807:
6801:
6800:
6774:
6768:
6767:
6739:
6715:
6709:
6708:
6680:
6660:
6654:
6653:
6625:
6605:
6599:
6598:
6578:
6554:
6548:
6547:
6527:
6503:
6497:
6482:
6476:
6475:
6457:
6437:
6431:
6430:
6401:; Kröger, Peer;
6395:
6389:
6382:
6376:
6373:
6367:
6366:
6334:
6328:
6327:
6319:
6313:
6312:
6286:
6280:
6279:
6259:
6243:
6234:
6233:
6231:
6207:
6201:
6200:
6174:
6168:
6155:
6149:
6148:
6109:
6103:
6102:
6074:
6068:
6067:
6047:
6041:
6040:
6022:
6013:
6007:
6006:
5993:Cluster analysis
5988:
5982:
5981:
5971:
5961:
5936:
5930:
5920:
5914:
5913:
5885:
5868:
5867:
5864:10.1037/h0054116
5847:
5841:
5840:
5833:Tryon, Robert C.
5829:
5823:
5822:
5811:10.1037/h0055441
5794:
5788:
5787:
5785:
5784:
5764:
5432:border detection
5403:Computer science
5366:In the study of
5244:High-throughput
5239:gene duplication
5135:
5128:
5124:
5121:
5115:
5092:
5084:
5059:
5057:
5056:
5051:
5045:
5043:
5042:
5040:
5035:
5024:
5019:
5001:
4999:
4994:
4983:
4978:
4962:
4961:
4959:
4954:
4943:
4938:
4922:
4905:
4903:
4902:
4897:
4889:
4888:
4872:
4870:
4869:
4864:
4862:
4861:
4845:
4843:
4842:
4837:
4829:
4828:
4812:
4810:
4809:
4804:
4802:
4801:
4785:
4783:
4782:
4777:
4765:
4763:
4762:
4757:
4745:
4743:
4742:
4737:
4735:
4734:
4718:
4716:
4715:
4710:
4692:
4690:
4689:
4684:
4672:
4670:
4669:
4664:
4652:
4650:
4649:
4644:
4611:Cluster tendency
4599:Confusion matrix
4548:
4546:
4545:
4540:
4538:
4537:
4518:
4516:
4515:
4510:
4508:
4507:
4479:
4477:
4476:
4471:
4459:
4457:
4456:
4451:
4432:
4430:
4429:
4424:
4405:
4403:
4402:
4397:
4378:
4376:
4375:
4370:
4351:
4349:
4348:
4343:
4324:
4322:
4321:
4316:
4314:
4312:
4310:
4293:
4285:
4280:
4278:
4261:
4253:
4251:
4215:
4213:
4212:
4207:
4205:
4203:
4174:
4163:
4140:
4138:
4137:
4132:
4117:
4115:
4114:
4109:
4080:
4078:
4077:
4072:
4050:
4048:
4047:
4042:
4040:
4038:
4012:
4004:
3999:
3997:
3996:
3982:
3976:
3975:
3961:
3955:
3904:
3902:
3901:
3896:
3878:
3876:
3875:
3870:
3858:
3856:
3855:
3850:
3828:
3826:
3825:
3820:
3812:
3811:
3795:
3793:
3792:
3787:
3765:
3763:
3762:
3757:
3755:
3753:
3740:
3739:
3729:
3707:
3706:
3693:
3688:
3687:
3665:
3663:
3662:
3657:
3641:
3639:
3638:
3633:
3617:
3615:
3614:
3609:
3607:
3605:
3588:
3580:
3564:
3562:
3561:
3556:
3554:
3552:
3535:
3527:
3498:
3496:
3495:
3490:
3431:
3429:
3428:
3423:
3421:
3420:
3419:
3406:
3356:
3354:
3353:
3348:
3333:
3331:
3330:
3325:
3302:
3300:
3299:
3294:
3275:
3273:
3272:
3267:
3248:
3246:
3245:
3240:
3225:
3223:
3222:
3217:
3198:
3196:
3195:
3190:
3188:
3186:
3151:
3134:
3095:
3093:
3092:
3087:
3085:
3084:
3070:
3063:
3047:
3032:
3024:
3009:
3007:
3006:
3001:
2989:
2987:
2986:
2981:
2969:
2967:
2966:
2961:
2912:thought of as a
2818:
2816:
2815:
2810:
2804:
2802:
2792:
2791:
2781:
2759:
2739:
2711:
2675:
2673:
2672:
2667:
2665:
2664:
2648:
2646:
2645:
2640:
2638:
2637:
2621:
2619:
2618:
2613:
2608:
2607:
2595:
2594:
2572:
2570:
2569:
2564:
2562:
2561:
2545:
2543:
2542:
2537:
2525:
2523:
2522:
2517:
2515:
2514:
2498:
2496:
2495:
2490:
2474:
2472:
2471:
2466:
2464:
2463:
2439:
2437:
2436:
2431:
2429:
2425:
2423:
2419:
2418:
2406:
2405:
2389:
2388:
2387:
2375:
2374:
2364:
2357:
2341:
2336:
2321:
2313:
2208:computer science
2060:
2045:
2030:
1987:
1985:
1984:
1979:
1959:
1957:
1956:
1951:
1911:
1901:
1895:
1805:
1790:
1769:machine learning
1655:
1643:
1630:
1628:
1627:
1622:
1617:
1616:
1604:
1603:
1586:
1584:
1583:
1578:
1573:
1572:
1554:
1553:
1540:
1538:
1537:
1532:
1527:
1526:
1514:
1513:
1416:
1415:
1404:
1403:
1386:
1385:
1373:
1372:
1363:
1362:
1348:
1347:
1345:fuzzy clustering
1338:
1337:
1328:
1327:
1292:
1291:
1242:
1241:
1231:
1230:
1216:
1215:
1197:
1196:
1178:
1177:
1163:
1162:
1148:
1147:
1100:
1097:
1094:
1091:
1023:machine learning
1015:data compression
972:Cluster analysis
961:
954:
947:
908:Related articles
785:Confusion matrix
538:Isolation forest
483:Graphical models
262:
261:
214:Learning to rank
209:Feature learning
47:Machine learning
38:
37:
21:
11287:
11286:
11282:
11281:
11280:
11278:
11277:
11276:
11252:
11251:
11250:
11245:
11208:
11179:
11141:
11078:
11064:quality control
11031:
11013:Clinical trials
10990:
10965:
10949:
10937:Hazard function
10931:
10885:
10847:
10831:
10794:
10790:BreuschâGodfrey
10778:
10755:
10695:
10670:Factor analysis
10616:
10597:Graphical model
10569:
10536:
10503:
10489:
10469:
10423:
10390:
10352:
10315:
10314:
10283:
10227:
10214:
10206:
10198:
10182:
10167:
10146:Rank statistics
10140:
10119:Model selection
10107:
10065:Goodness of fit
10059:
10036:
10010:
9982:
9935:
9880:
9869:Median unbiased
9797:
9708:
9641:Order statistic
9603:
9582:
9549:
9523:
9475:
9430:
9373:
9371:Data collection
9352:
9264:
9219:
9193:
9171:
9131:
9083:
9000:Continuous data
8990:
8977:
8959:
8954:
8924:
8919:
8871:
8785:
8751:Google DeepMind
8729:
8695:Geoffrey Hinton
8654:
8591:
8517:Project Debater
8463:
8361:Implementations
8356:
8310:
8274:
8217:
8159:Backpropagation
8093:
8079:Tensor calculus
8033:
8030:
8000:
7999:
7968:
7964:
7916:
7910:
7906:
7877:
7873:
7858:
7854:
7840:
7838:
7829:
7828:
7825:
7824:
7820:
7813:
7809:
7778:
7771:
7726:
7722:
7677:Genome Research
7669:
7665:
7626:
7622:
7591:
7587:
7540:
7536:
7521:
7499:
7495:
7472:
7468:
7423:
7419:
7408:
7404:
7381:
7377:
7346:
7342:
7331:
7327:
7296:
7292:
7277:10.2307/2284239
7249:
7245:
7222:
7218:
7195:
7181:
7174:
7151:
7147:
7124:
7120:
7104:
7103:
7094:
7088:
7087:
7080:
7073:
7059:
7050:
7035:
7021:
7014:
6999:
6985:
6976:
6945:
6934:
6902:10.1.1.121.3145
6877:
6873:
6858:
6854:
6842:
6840:
6831:
6830:
6808:
6804:
6797:
6775:
6771:
6756:
6716:
6712:
6697:
6671:. p. 455.
6661:
6657:
6642:
6623:10.1.1.707.7872
6606:
6602:
6595:
6555:
6551:
6544:
6525:10.1.1.705.2956
6504:
6500:
6484:Karin Kailing,
6483:
6479:
6455:10.1.1.131.5152
6438:
6434:
6396:
6392:
6383:
6379:
6374:
6370:
6335:
6331:
6320:
6316:
6301:
6287:
6283:
6276:
6244:
6237:
6229:10.1.1.129.6542
6208:
6204:
6197:
6177:Ester, Martin;
6175:
6171:
6165:Wayback Machine
6156:
6152:
6137:10.1002/widm.30
6110:
6106:
6075:
6071:
6048:
6044:
6020:
6014:
6010:
6003:
5989:
5985:
5937:
5933:
5927:Human Relations
5921:
5917:
5886:
5871:
5848:
5844:
5830:
5826:
5795:
5791:
5782:
5780:
5765:
5761:
5756:
5719:
5697:
5664:
5602:
5597:
5536:
5498:
5406:
5368:social networks
5360:
5308:Market research
5304:
5272:Medical imaging
5268:
5204:DNA microarrays
5194:for a specific
5178:Transcriptomics
5151:
5145:
5136:
5125:
5119:
5116:
5105:
5093:
5082:
5036:
5031:
5026:
5020:
5009:
4995:
4990:
4985:
4979:
4968:
4963:
4955:
4950:
4945:
4939:
4928:
4923:
4921:
4913:
4910:
4909:
4884:
4880:
4878:
4875:
4874:
4857:
4853:
4851:
4848:
4847:
4824:
4820:
4818:
4815:
4814:
4797:
4793:
4791:
4788:
4787:
4771:
4768:
4767:
4751:
4748:
4747:
4730:
4726:
4724:
4721:
4720:
4698:
4695:
4694:
4678:
4675:
4674:
4673:data points in
4658:
4655:
4654:
4638:
4635:
4634:
4613:
4530:
4526:
4524:
4521:
4520:
4503:
4499:
4497:
4494:
4493:
4465:
4462:
4461:
4445:
4442:
4441:
4415:
4412:
4411:
4408:false negatives
4388:
4385:
4384:
4381:false positives
4361:
4358:
4357:
4334:
4331:
4330:
4294:
4286:
4284:
4262:
4254:
4252:
4250:
4239:
4236:
4235:
4175:
4164:
4162:
4148:
4145:
4144:
4123:
4120:
4119:
4100:
4097:
4096:
4063:
4060:
4059:
4013:
4005:
4003:
3992:
3978:
3977:
3971:
3957:
3956:
3954:
3931:
3928:
3927:
3887:
3884:
3883:
3864:
3861:
3860:
3838:
3835:
3834:
3807:
3803:
3801:
3798:
3797:
3775:
3772:
3771:
3735:
3731:
3730:
3702:
3698:
3694:
3692:
3683:
3679:
3677:
3674:
3673:
3651:
3648:
3647:
3627:
3624:
3623:
3589:
3581:
3579:
3571:
3568:
3567:
3536:
3528:
3526:
3518:
3515:
3514:
3478:
3475:
3474:
3467:false negatives
3446:false negatives
3442:false positives
3415:
3402:
3401:
3400:
3362:
3359:
3358:
3339:
3336:
3335:
3316:
3313:
3312:
3305:false negatives
3285:
3282:
3281:
3278:false positives
3258:
3255:
3254:
3231:
3228:
3227:
3208:
3205:
3204:
3152:
3135:
3133:
3122:
3119:
3118:
3080:
3066:
3065:
3053:
3037:
3023:
3021:
3018:
3017:
2995:
2992:
2991:
2975:
2972:
2971:
2955:
2952:
2951:
2909:
2787:
2783:
2765:
2760:
2717:
2712:
2710:
2702:
2699:
2698:
2660:
2656:
2654:
2651:
2650:
2633:
2629:
2627:
2624:
2623:
2603:
2599:
2590:
2586:
2578:
2575:
2574:
2557:
2553:
2551:
2548:
2547:
2531:
2528:
2527:
2510:
2506:
2504:
2501:
2500:
2484:
2481:
2480:
2459:
2455:
2453:
2450:
2449:
2414:
2410:
2401:
2397:
2390:
2383:
2379:
2370:
2366:
2365:
2363:
2359:
2347:
2337:
2326:
2312:
2301:
2298:
2297:
2262:
2256:
2220:
2122:
2074:
2067:
2061:
2052:
2046:
2037:
2031:
1973:
1970:
1969:
1945:
1942:
1941:
1922:
1915:
1912:
1903:
1896:
1834:
1812:
1806:
1797:
1791:
1765:Voronoi diagram
1672:
1666:
1659:
1656:
1647:
1644:
1612:
1608:
1599:
1598:
1596:
1593:
1592:
1562:
1558:
1549:
1548:
1546:
1543:
1542:
1522:
1518:
1509:
1508:
1506:
1503:
1502:
1454:
1448:
1431:
1427:Main category:
1425:
1411:
1410:
1401:
1400:
1383:
1382:
1370:
1369:
1360:
1359:
1343:
1342:
1336:Soft clustering
1335:
1334:
1326:Hard clustering
1325:
1324:
1289:
1288:
1239:
1238:
1228:
1227:
1213:
1212:
1200:: for example,
1194:
1193:
1175:
1174:
1160:
1159:
1151:: for example,
1145:
1144:
1136:
1098:
1095:
1092:
965:
936:
935:
909:
901:
900:
861:
853:
852:
813:Kernel machines
808:
800:
799:
775:
767:
766:
747:Active learning
742:
734:
733:
702:
692:
691:
617:Diffusion model
553:
543:
542:
515:
505:
504:
478:
468:
467:
423:Factor analysis
418:
408:
407:
391:
354:
344:
343:
264:
263:
247:
246:
245:
234:
233:
139:
131:
130:
96:Online learning
61:
49:
28:
23:
22:
15:
12:
11:
5:
11285:
11275:
11274:
11269:
11264:
11247:
11246:
11244:
11243:
11231:
11219:
11205:
11192:
11189:
11188:
11185:
11184:
11181:
11180:
11178:
11177:
11172:
11167:
11162:
11157:
11151:
11149:
11143:
11142:
11140:
11139:
11134:
11129:
11124:
11119:
11114:
11109:
11104:
11099:
11094:
11088:
11086:
11080:
11079:
11077:
11076:
11071:
11066:
11057:
11052:
11047:
11041:
11039:
11033:
11032:
11030:
11029:
11024:
11019:
11010:
11008:Bioinformatics
11004:
11002:
10992:
10991:
10979:
10978:
10975:
10974:
10971:
10970:
10967:
10966:
10964:
10963:
10957:
10955:
10951:
10950:
10948:
10947:
10941:
10939:
10933:
10932:
10930:
10929:
10924:
10919:
10914:
10908:
10906:
10897:
10891:
10890:
10887:
10886:
10884:
10883:
10878:
10873:
10868:
10863:
10857:
10855:
10849:
10848:
10846:
10845:
10840:
10835:
10827:
10822:
10817:
10816:
10815:
10813:partial (PACF)
10804:
10802:
10796:
10795:
10793:
10792:
10787:
10782:
10774:
10769:
10763:
10761:
10760:Specific tests
10757:
10756:
10754:
10753:
10748:
10743:
10738:
10733:
10728:
10723:
10718:
10712:
10710:
10703:
10697:
10696:
10694:
10693:
10692:
10691:
10690:
10689:
10674:
10673:
10672:
10662:
10660:Classification
10657:
10652:
10647:
10642:
10637:
10632:
10626:
10624:
10618:
10617:
10615:
10614:
10609:
10607:McNemar's test
10604:
10599:
10594:
10589:
10583:
10581:
10571:
10570:
10546:
10545:
10542:
10541:
10538:
10537:
10535:
10534:
10529:
10524:
10519:
10513:
10511:
10505:
10504:
10502:
10501:
10485:
10479:
10477:
10471:
10470:
10468:
10467:
10462:
10457:
10452:
10447:
10445:Semiparametric
10442:
10437:
10431:
10429:
10425:
10424:
10422:
10421:
10416:
10411:
10406:
10400:
10398:
10392:
10391:
10389:
10388:
10383:
10378:
10373:
10368:
10362:
10360:
10354:
10353:
10351:
10350:
10345:
10340:
10335:
10329:
10327:
10317:
10316:
10313:
10312:
10307:
10301:
10293:
10292:
10289:
10288:
10285:
10284:
10282:
10281:
10280:
10279:
10269:
10264:
10259:
10258:
10257:
10252:
10241:
10239:
10233:
10232:
10229:
10228:
10226:
10225:
10220:
10219:
10218:
10210:
10202:
10186:
10183:(MannâWhitney)
10178:
10177:
10176:
10163:
10162:
10161:
10150:
10148:
10142:
10141:
10139:
10138:
10137:
10136:
10131:
10126:
10116:
10111:
10108:(ShapiroâWilk)
10103:
10098:
10093:
10088:
10083:
10075:
10069:
10067:
10061:
10060:
10058:
10057:
10049:
10040:
10028:
10022:
10020:Specific tests
10016:
10015:
10012:
10011:
10009:
10008:
10003:
9998:
9992:
9990:
9984:
9983:
9981:
9980:
9975:
9974:
9973:
9963:
9962:
9961:
9951:
9945:
9943:
9937:
9936:
9934:
9933:
9932:
9931:
9926:
9916:
9911:
9906:
9901:
9896:
9890:
9888:
9882:
9881:
9879:
9878:
9873:
9872:
9871:
9866:
9865:
9864:
9859:
9844:
9843:
9842:
9837:
9832:
9827:
9816:
9814:
9805:
9799:
9798:
9796:
9795:
9790:
9785:
9784:
9783:
9773:
9768:
9767:
9766:
9756:
9755:
9754:
9749:
9744:
9734:
9729:
9724:
9723:
9722:
9717:
9712:
9696:
9695:
9694:
9689:
9684:
9674:
9673:
9672:
9667:
9657:
9656:
9655:
9645:
9644:
9643:
9633:
9628:
9623:
9617:
9615:
9605:
9604:
9592:
9591:
9588:
9587:
9584:
9583:
9581:
9580:
9575:
9570:
9565:
9559:
9557:
9551:
9550:
9548:
9547:
9542:
9537:
9531:
9529:
9525:
9524:
9522:
9521:
9516:
9511:
9506:
9501:
9496:
9491:
9485:
9483:
9477:
9476:
9474:
9473:
9471:Standard error
9468:
9463:
9458:
9457:
9456:
9451:
9440:
9438:
9432:
9431:
9429:
9428:
9423:
9418:
9413:
9408:
9403:
9401:Optimal design
9398:
9393:
9387:
9385:
9375:
9374:
9362:
9361:
9358:
9357:
9354:
9353:
9351:
9350:
9345:
9340:
9335:
9330:
9325:
9320:
9315:
9310:
9305:
9300:
9295:
9290:
9285:
9280:
9274:
9272:
9266:
9265:
9263:
9262:
9257:
9256:
9255:
9250:
9240:
9235:
9229:
9227:
9221:
9220:
9218:
9217:
9212:
9207:
9201:
9199:
9198:Summary tables
9195:
9194:
9192:
9191:
9185:
9183:
9177:
9176:
9173:
9172:
9170:
9169:
9168:
9167:
9162:
9157:
9147:
9141:
9139:
9133:
9132:
9130:
9129:
9124:
9119:
9114:
9109:
9104:
9099:
9093:
9091:
9085:
9084:
9082:
9081:
9076:
9071:
9070:
9069:
9064:
9059:
9054:
9049:
9044:
9039:
9034:
9032:Contraharmonic
9029:
9024:
9013:
9011:
9002:
8992:
8991:
8979:
8978:
8976:
8975:
8970:
8964:
8961:
8960:
8953:
8952:
8945:
8938:
8930:
8921:
8920:
8918:
8917:
8916:
8915:
8910:
8897:
8896:
8895:
8890:
8876:
8873:
8872:
8870:
8869:
8864:
8859:
8854:
8849:
8844:
8839:
8834:
8829:
8824:
8819:
8814:
8809:
8804:
8799:
8793:
8791:
8787:
8786:
8784:
8783:
8778:
8773:
8768:
8763:
8758:
8753:
8748:
8743:
8737:
8735:
8731:
8730:
8728:
8727:
8725:Ilya Sutskever
8722:
8717:
8712:
8707:
8702:
8697:
8692:
8690:Demis Hassabis
8687:
8682:
8680:Ian Goodfellow
8677:
8672:
8666:
8664:
8660:
8659:
8656:
8655:
8653:
8652:
8647:
8646:
8645:
8635:
8630:
8625:
8620:
8615:
8610:
8605:
8599:
8597:
8593:
8592:
8590:
8589:
8584:
8579:
8574:
8569:
8564:
8559:
8554:
8549:
8544:
8539:
8534:
8529:
8524:
8519:
8514:
8509:
8508:
8507:
8497:
8492:
8487:
8482:
8477:
8471:
8469:
8465:
8464:
8462:
8461:
8456:
8455:
8454:
8449:
8439:
8438:
8437:
8432:
8427:
8417:
8412:
8407:
8402:
8397:
8392:
8387:
8382:
8377:
8371:
8369:
8362:
8358:
8357:
8355:
8354:
8349:
8344:
8339:
8334:
8329:
8324:
8318:
8316:
8312:
8311:
8309:
8308:
8303:
8298:
8293:
8288:
8282:
8280:
8276:
8275:
8273:
8272:
8271:
8270:
8263:Language model
8260:
8255:
8250:
8249:
8248:
8238:
8237:
8236:
8225:
8223:
8219:
8218:
8216:
8215:
8213:Autoregression
8210:
8205:
8204:
8203:
8193:
8191:Regularization
8188:
8187:
8186:
8181:
8176:
8166:
8161:
8156:
8154:Loss functions
8151:
8146:
8141:
8136:
8131:
8130:
8129:
8119:
8114:
8113:
8112:
8101:
8099:
8095:
8094:
8092:
8091:
8089:Inductive bias
8086:
8081:
8076:
8071:
8066:
8061:
8056:
8051:
8043:
8041:
8035:
8034:
8029:
8028:
8021:
8014:
8006:
7998:
7997:
7962:
7927:(1): 105â152.
7904:
7891:(1â3): 17â44.
7871:
7852:
7841:|journal=
7818:
7807:
7788:(3): 709â754.
7769:
7720:
7663:
7620:
7601:(4): 175â181.
7585:
7550:(3): 241â254.
7534:
7519:
7493:
7466:
7417:
7402:
7375:
7340:
7325:
7290:
7243:
7216:
7172:
7145:
7118:
7078:
7071:
7048:
7034:978-0387954332
7033:
7012:
6998:978-0521836579
6997:
6974:
6932:
6871:
6852:
6843:|journal=
6802:
6795:
6769:
6754:
6737:10.1.1.71.5021
6722:; Kröger, P.;
6720:Kriegel, H. P.
6710:
6696:978-1581138597
6695:
6655:
6640:
6600:
6593:
6576:10.1.1.70.7843
6559:Kriegel, H. P.
6549:
6542:
6508:Kriegel, H.-P.
6498:
6477:
6432:
6413:(4): 351â364.
6390:
6377:
6368:
6349:(3): 283â304.
6329:
6314:
6299:
6281:
6274:
6257:10.1.1.64.1161
6235:
6202:
6195:
6169:
6150:
6131:(3): 231â240.
6104:
6085:(2): 129â137.
6069:
6042:
6008:
6001:
5983:
5931:
5923:James A. Davis
5915:
5869:
5858:(4): 476â506.
5842:
5824:
5805:(4): 508â516.
5789:
5758:
5757:
5755:
5752:
5751:
5750:
5745:
5740:
5735:
5730:
5725:
5718:
5715:
5714:
5713:
5708:
5703:
5696:
5693:
5692:
5691:
5686:
5681:
5676:
5671:
5663:
5660:
5659:
5658:
5653:
5648:
5646:HCS clustering
5643:
5638:
5633:
5628:
5623:
5618:
5613:
5608:
5601:
5598:
5596:
5593:
5592:
5591:
5588:
5584:
5583:
5580:
5576:
5575:
5572:
5568:
5567:
5564:
5558:
5557:
5550:
5544:
5543:
5540:
5539:Field robotics
5535:
5532:
5531:
5530:
5527:
5524:
5521:
5516:
5512:
5510:Crime analysis
5507:
5504:
5497:
5496:Social science
5494:
5493:
5492:
5489:
5484:
5477:
5472:
5469:
5464:
5461:
5456:
5452:
5447:
5444:
5439:
5421:
5416:
5412:
5405:
5400:
5399:
5398:
5392:
5389:
5378:
5375:
5364:
5359:
5357:World Wide Web
5354:
5353:
5352:
5344:
5341:
5310:
5303:
5300:
5299:
5298:
5295:
5292:
5288:
5285:
5274:
5267:
5262:
5261:
5260:
5257:
5252:
5249:
5242:
5231:bioinformatics
5220:
5215:
5180:
5175:
5164:
5144:
5141:
5138:
5137:
5096:
5094:
5087:
5081:
5078:
5077:
5076:
5065:
5062:
5061:
5060:
5049:
5039:
5034:
5030:
5023:
5018:
5015:
5012:
5008:
5004:
4998:
4993:
4989:
4982:
4977:
4974:
4971:
4967:
4958:
4953:
4949:
4942:
4937:
4934:
4931:
4927:
4920:
4917:
4895:
4892:
4887:
4883:
4860:
4856:
4835:
4832:
4827:
4823:
4800:
4796:
4775:
4755:
4733:
4729:
4708:
4705:
4702:
4682:
4662:
4653:be the set of
4642:
4626:
4625:
4612:
4609:
4608:
4607:
4603:
4602:
4595:
4578:
4567:
4566:
4536:
4533:
4529:
4506:
4502:
4469:
4449:
4422:
4419:
4395:
4392:
4368:
4365:
4354:true positives
4341:
4338:
4327:
4326:
4325:
4309:
4306:
4303:
4300:
4297:
4292:
4289:
4283:
4277:
4274:
4271:
4268:
4265:
4260:
4257:
4249:
4246:
4243:
4228:
4227:
4219:
4218:
4217:
4216:
4202:
4199:
4196:
4193:
4190:
4187:
4184:
4181:
4178:
4173:
4170:
4167:
4161:
4158:
4155:
4152:
4130:
4127:
4107:
4104:
4092:
4091:
4083:
4082:
4070:
4067:
4056:
4053:
4052:
4051:
4037:
4034:
4031:
4028:
4025:
4022:
4019:
4016:
4011:
4008:
4002:
3995:
3991:
3988:
3985:
3981:
3974:
3970:
3967:
3964:
3960:
3953:
3950:
3947:
3944:
3941:
3938:
3935:
3916:
3915:
3907:
3906:
3894:
3891:
3880:
3868:
3848:
3845:
3842:
3818:
3815:
3810:
3806:
3785:
3782:
3779:
3768:
3767:
3766:
3752:
3749:
3746:
3743:
3738:
3734:
3728:
3725:
3722:
3719:
3716:
3713:
3710:
3705:
3701:
3697:
3691:
3686:
3682:
3655:
3631:
3620:
3619:
3618:
3604:
3601:
3598:
3595:
3592:
3587:
3584:
3578:
3575:
3565:
3551:
3548:
3545:
3542:
3539:
3534:
3531:
3525:
3522:
3488:
3485:
3482:
3462:
3461:
3434:
3433:
3418:
3413:
3410:
3405:
3399:
3396:
3393:
3390:
3387:
3384:
3381:
3378:
3375:
3372:
3369:
3366:
3346:
3343:
3323:
3320:
3292:
3289:
3265:
3262:
3251:true negatives
3238:
3235:
3215:
3212:
3201:
3200:
3199:
3185:
3182:
3179:
3176:
3173:
3170:
3167:
3164:
3161:
3158:
3155:
3150:
3147:
3144:
3141:
3138:
3132:
3129:
3126:
3112:
3111:
3103:
3102:
3098:
3097:
3096:
3083:
3079:
3076:
3073:
3069:
3062:
3059:
3056:
3052:
3046:
3043:
3040:
3036:
3030:
3027:
3012:
3011:
2999:
2979:
2959:
2934:true positives
2908:
2905:
2904:
2903:
2894:
2893:
2885:
2884:
2821:
2820:
2819:
2808:
2801:
2798:
2795:
2790:
2786:
2780:
2777:
2774:
2771:
2768:
2764:
2758:
2755:
2752:
2749:
2746:
2743:
2738:
2735:
2732:
2729:
2726:
2723:
2720:
2716:
2709:
2706:
2691:
2690:
2682:
2681:
2663:
2659:
2636:
2632:
2611:
2606:
2602:
2598:
2593:
2589:
2585:
2582:
2560:
2556:
2535:
2513:
2509:
2488:
2462:
2458:
2442:
2441:
2440:
2428:
2422:
2417:
2413:
2409:
2404:
2400:
2396:
2393:
2386:
2382:
2378:
2373:
2369:
2362:
2356:
2353:
2350:
2346:
2340:
2335:
2332:
2329:
2325:
2319:
2316:
2311:
2308:
2305:
2287:
2286:
2255:
2252:
2219:
2216:
2161:that focus on
2121:
2118:
2117:
2116:
2113:
2110:
2109:
2108:
2105:
2102:
2096:
2093:
2090:
2073:
2070:
2069:
2068:
2062:
2055:
2053:
2047:
2040:
2038:
2032:
2025:
2023:
1977:
1949:
1921:
1918:
1917:
1916:
1913:
1906:
1904:
1897:
1890:
1888:
1833:
1830:
1814:
1813:
1807:
1800:
1798:
1792:
1785:
1783:
1668:Main article:
1665:
1662:
1661:
1660:
1657:
1650:
1648:
1645:
1638:
1636:
1620:
1615:
1611:
1607:
1602:
1576:
1571:
1568:
1565:
1561:
1557:
1552:
1530:
1525:
1521:
1517:
1512:
1450:Main article:
1447:
1444:
1424:
1421:
1420:
1419:
1407:
1397:
1379:
1366:
1352:
1351:
1331:
1316:
1315:
1300:neural network
1285:
1278:balance theory
1259:
1235:
1224:
1214:Subspace model
1209:
1190:
1171:
1161:Centroid model
1156:
1135:
1132:
1071:classification
1011:bioinformatics
1003:image analysis
967:
966:
964:
963:
956:
949:
941:
938:
937:
934:
933:
928:
927:
926:
916:
910:
907:
906:
903:
902:
899:
898:
893:
888:
883:
878:
873:
868:
862:
859:
858:
855:
854:
851:
850:
845:
840:
835:
833:Occam learning
830:
825:
820:
815:
809:
806:
805:
802:
801:
798:
797:
792:
790:Learning curve
787:
782:
776:
773:
772:
769:
768:
765:
764:
759:
754:
749:
743:
740:
739:
736:
735:
732:
731:
730:
729:
719:
714:
709:
703:
698:
697:
694:
693:
690:
689:
683:
678:
673:
668:
667:
666:
656:
651:
650:
649:
644:
639:
634:
624:
619:
614:
609:
608:
607:
597:
596:
595:
590:
585:
580:
570:
565:
560:
554:
549:
548:
545:
544:
541:
540:
535:
530:
522:
516:
511:
510:
507:
506:
503:
502:
501:
500:
495:
490:
479:
474:
473:
470:
469:
466:
465:
460:
455:
450:
445:
440:
435:
430:
425:
419:
414:
413:
410:
409:
406:
405:
400:
395:
389:
384:
379:
371:
366:
361:
355:
350:
349:
346:
345:
342:
341:
336:
331:
326:
321:
316:
311:
306:
298:
297:
296:
291:
286:
276:
274:Decision trees
271:
265:
251:classification
241:
240:
239:
236:
235:
232:
231:
226:
221:
216:
211:
206:
201:
196:
191:
186:
181:
176:
171:
166:
161:
156:
151:
146:
144:Classification
140:
137:
136:
133:
132:
129:
128:
123:
118:
113:
108:
103:
101:Batch learning
98:
93:
88:
83:
78:
73:
68:
62:
59:
58:
55:
54:
43:
42:
26:
9:
6:
4:
3:
2:
11284:
11273:
11272:Geostatistics
11270:
11268:
11265:
11263:
11260:
11259:
11257:
11242:
11241:
11232:
11230:
11229:
11220:
11218:
11217:
11212:
11206:
11204:
11203:
11194:
11193:
11190:
11176:
11173:
11171:
11170:Geostatistics
11168:
11166:
11163:
11161:
11158:
11156:
11153:
11152:
11150:
11148:
11144:
11138:
11137:Psychometrics
11135:
11133:
11130:
11128:
11125:
11123:
11120:
11118:
11115:
11113:
11110:
11108:
11105:
11103:
11100:
11098:
11095:
11093:
11090:
11089:
11087:
11085:
11081:
11075:
11072:
11070:
11067:
11065:
11061:
11058:
11056:
11053:
11051:
11048:
11046:
11043:
11042:
11040:
11038:
11034:
11028:
11025:
11023:
11020:
11018:
11014:
11011:
11009:
11006:
11005:
11003:
11001:
11000:Biostatistics
10997:
10993:
10989:
10984:
10980:
10962:
10961:Log-rank test
10959:
10958:
10956:
10952:
10946:
10943:
10942:
10940:
10938:
10934:
10928:
10925:
10923:
10920:
10918:
10915:
10913:
10910:
10909:
10907:
10905:
10901:
10898:
10896:
10892:
10882:
10879:
10877:
10874:
10872:
10869:
10867:
10864:
10862:
10859:
10858:
10856:
10854:
10850:
10844:
10841:
10839:
10836:
10834:
10832:(BoxâJenkins)
10828:
10826:
10823:
10821:
10818:
10814:
10811:
10810:
10809:
10806:
10805:
10803:
10801:
10797:
10791:
10788:
10786:
10785:DurbinâWatson
10783:
10781:
10775:
10773:
10770:
10768:
10767:DickeyâFuller
10765:
10764:
10762:
10758:
10752:
10749:
10747:
10744:
10742:
10741:Cointegration
10739:
10737:
10734:
10732:
10729:
10727:
10724:
10722:
10719:
10717:
10716:Decomposition
10714:
10713:
10711:
10707:
10704:
10702:
10698:
10688:
10685:
10684:
10683:
10680:
10679:
10678:
10675:
10671:
10668:
10667:
10666:
10663:
10661:
10658:
10656:
10653:
10651:
10648:
10646:
10643:
10641:
10638:
10636:
10633:
10631:
10628:
10627:
10625:
10623:
10619:
10613:
10610:
10608:
10605:
10603:
10600:
10598:
10595:
10593:
10590:
10588:
10587:Cohen's kappa
10585:
10584:
10582:
10580:
10576:
10572:
10568:
10564:
10560:
10556:
10551:
10547:
10533:
10530:
10528:
10525:
10523:
10520:
10518:
10515:
10514:
10512:
10510:
10506:
10500:
10496:
10492:
10486:
10484:
10481:
10480:
10478:
10476:
10472:
10466:
10463:
10461:
10458:
10456:
10453:
10451:
10448:
10446:
10443:
10441:
10440:Nonparametric
10438:
10436:
10433:
10432:
10430:
10426:
10420:
10417:
10415:
10412:
10410:
10407:
10405:
10402:
10401:
10399:
10397:
10393:
10387:
10384:
10382:
10379:
10377:
10374:
10372:
10369:
10367:
10364:
10363:
10361:
10359:
10355:
10349:
10346:
10344:
10341:
10339:
10336:
10334:
10331:
10330:
10328:
10326:
10322:
10318:
10311:
10308:
10306:
10303:
10302:
10298:
10294:
10278:
10275:
10274:
10273:
10270:
10268:
10265:
10263:
10260:
10256:
10253:
10251:
10248:
10247:
10246:
10243:
10242:
10240:
10238:
10234:
10224:
10221:
10217:
10211:
10209:
10203:
10201:
10195:
10194:
10193:
10190:
10189:Nonparametric
10187:
10185:
10179:
10175:
10172:
10171:
10170:
10164:
10160:
10159:Sample median
10157:
10156:
10155:
10152:
10151:
10149:
10147:
10143:
10135:
10132:
10130:
10127:
10125:
10122:
10121:
10120:
10117:
10115:
10112:
10110:
10104:
10102:
10099:
10097:
10094:
10092:
10089:
10087:
10084:
10082:
10080:
10076:
10074:
10071:
10070:
10068:
10066:
10062:
10056:
10054:
10050:
10048:
10046:
10041:
10039:
10034:
10030:
10029:
10026:
10023:
10021:
10017:
10007:
10004:
10002:
9999:
9997:
9994:
9993:
9991:
9989:
9985:
9979:
9976:
9972:
9969:
9968:
9967:
9964:
9960:
9957:
9956:
9955:
9952:
9950:
9947:
9946:
9944:
9942:
9938:
9930:
9927:
9925:
9922:
9921:
9920:
9917:
9915:
9912:
9910:
9907:
9905:
9902:
9900:
9897:
9895:
9892:
9891:
9889:
9887:
9883:
9877:
9874:
9870:
9867:
9863:
9860:
9858:
9855:
9854:
9853:
9850:
9849:
9848:
9845:
9841:
9838:
9836:
9833:
9831:
9828:
9826:
9823:
9822:
9821:
9818:
9817:
9815:
9813:
9809:
9806:
9804:
9800:
9794:
9791:
9789:
9786:
9782:
9779:
9778:
9777:
9774:
9772:
9769:
9765:
9764:loss function
9762:
9761:
9760:
9757:
9753:
9750:
9748:
9745:
9743:
9740:
9739:
9738:
9735:
9733:
9730:
9728:
9725:
9721:
9718:
9716:
9713:
9711:
9705:
9702:
9701:
9700:
9697:
9693:
9690:
9688:
9685:
9683:
9680:
9679:
9678:
9675:
9671:
9668:
9666:
9663:
9662:
9661:
9658:
9654:
9651:
9650:
9649:
9646:
9642:
9639:
9638:
9637:
9634:
9632:
9629:
9627:
9624:
9622:
9619:
9618:
9616:
9614:
9610:
9606:
9602:
9597:
9593:
9579:
9576:
9574:
9571:
9569:
9566:
9564:
9561:
9560:
9558:
9556:
9552:
9546:
9543:
9541:
9538:
9536:
9533:
9532:
9530:
9526:
9520:
9517:
9515:
9512:
9510:
9507:
9505:
9502:
9500:
9497:
9495:
9492:
9490:
9487:
9486:
9484:
9482:
9478:
9472:
9469:
9467:
9466:Questionnaire
9464:
9462:
9459:
9455:
9452:
9450:
9447:
9446:
9445:
9442:
9441:
9439:
9437:
9433:
9427:
9424:
9422:
9419:
9417:
9414:
9412:
9409:
9407:
9404:
9402:
9399:
9397:
9394:
9392:
9389:
9388:
9386:
9384:
9380:
9376:
9372:
9367:
9363:
9349:
9346:
9344:
9341:
9339:
9336:
9334:
9331:
9329:
9326:
9324:
9321:
9319:
9316:
9314:
9311:
9309:
9306:
9304:
9301:
9299:
9296:
9294:
9293:Control chart
9291:
9289:
9286:
9284:
9281:
9279:
9276:
9275:
9273:
9271:
9267:
9261:
9258:
9254:
9251:
9249:
9246:
9245:
9244:
9241:
9239:
9236:
9234:
9231:
9230:
9228:
9226:
9222:
9216:
9213:
9211:
9208:
9206:
9203:
9202:
9200:
9196:
9190:
9187:
9186:
9184:
9182:
9178:
9166:
9163:
9161:
9158:
9156:
9153:
9152:
9151:
9148:
9146:
9143:
9142:
9140:
9138:
9134:
9128:
9125:
9123:
9120:
9118:
9115:
9113:
9110:
9108:
9105:
9103:
9100:
9098:
9095:
9094:
9092:
9090:
9086:
9080:
9077:
9075:
9072:
9068:
9065:
9063:
9060:
9058:
9055:
9053:
9050:
9048:
9045:
9043:
9040:
9038:
9035:
9033:
9030:
9028:
9025:
9023:
9020:
9019:
9018:
9015:
9014:
9012:
9010:
9006:
9003:
9001:
8997:
8993:
8989:
8984:
8980:
8974:
8971:
8969:
8966:
8965:
8962:
8958:
8951:
8946:
8944:
8939:
8937:
8932:
8931:
8928:
8914:
8911:
8909:
8906:
8905:
8898:
8894:
8891:
8889:
8886:
8885:
8882:
8878:
8877:
8874:
8868:
8865:
8863:
8860:
8858:
8855:
8853:
8850:
8848:
8845:
8843:
8840:
8838:
8835:
8833:
8830:
8828:
8825:
8823:
8820:
8818:
8815:
8813:
8810:
8808:
8805:
8803:
8800:
8798:
8795:
8794:
8792:
8790:Architectures
8788:
8782:
8779:
8777:
8774:
8772:
8769:
8767:
8764:
8762:
8759:
8757:
8754:
8752:
8749:
8747:
8744:
8742:
8739:
8738:
8736:
8734:Organizations
8732:
8726:
8723:
8721:
8718:
8716:
8713:
8711:
8708:
8706:
8703:
8701:
8698:
8696:
8693:
8691:
8688:
8686:
8683:
8681:
8678:
8676:
8673:
8671:
8670:Yoshua Bengio
8668:
8667:
8665:
8661:
8651:
8650:Robot control
8648:
8644:
8641:
8640:
8639:
8636:
8634:
8631:
8629:
8626:
8624:
8621:
8619:
8616:
8614:
8611:
8609:
8606:
8604:
8601:
8600:
8598:
8594:
8588:
8585:
8583:
8580:
8578:
8575:
8573:
8570:
8568:
8567:Chinchilla AI
8565:
8563:
8560:
8558:
8555:
8553:
8550:
8548:
8545:
8543:
8540:
8538:
8535:
8533:
8530:
8528:
8525:
8523:
8520:
8518:
8515:
8513:
8510:
8506:
8503:
8502:
8501:
8498:
8496:
8493:
8491:
8488:
8486:
8483:
8481:
8478:
8476:
8473:
8472:
8470:
8466:
8460:
8457:
8453:
8450:
8448:
8445:
8444:
8443:
8440:
8436:
8433:
8431:
8428:
8426:
8423:
8422:
8421:
8418:
8416:
8413:
8411:
8408:
8406:
8403:
8401:
8398:
8396:
8393:
8391:
8388:
8386:
8383:
8381:
8378:
8376:
8373:
8372:
8370:
8366:
8363:
8359:
8353:
8350:
8348:
8345:
8343:
8340:
8338:
8335:
8333:
8330:
8328:
8325:
8323:
8320:
8319:
8317:
8313:
8307:
8304:
8302:
8299:
8297:
8294:
8292:
8289:
8287:
8284:
8283:
8281:
8277:
8269:
8266:
8265:
8264:
8261:
8259:
8256:
8254:
8251:
8247:
8246:Deep learning
8244:
8243:
8242:
8239:
8235:
8232:
8231:
8230:
8227:
8226:
8224:
8220:
8214:
8211:
8209:
8206:
8202:
8199:
8198:
8197:
8194:
8192:
8189:
8185:
8182:
8180:
8177:
8175:
8172:
8171:
8170:
8167:
8165:
8162:
8160:
8157:
8155:
8152:
8150:
8147:
8145:
8142:
8140:
8137:
8135:
8134:Hallucination
8132:
8128:
8125:
8124:
8123:
8120:
8118:
8115:
8111:
8108:
8107:
8106:
8103:
8102:
8100:
8096:
8090:
8087:
8085:
8082:
8080:
8077:
8075:
8072:
8070:
8067:
8065:
8062:
8060:
8057:
8055:
8052:
8050:
8049:
8045:
8044:
8042:
8040:
8036:
8027:
8022:
8020:
8015:
8013:
8008:
8007:
8004:
7993:
7989:
7985:
7981:
7977:
7973:
7966:
7958:
7954:
7950:
7946:
7942:
7938:
7934:
7930:
7926:
7922:
7915:
7908:
7899:
7894:
7890:
7886:
7882:
7875:
7867:
7863:
7856:
7850:
7846:
7833:
7822:
7816:
7811:
7803:
7799:
7795:
7791:
7787:
7783:
7776:
7774:
7765:
7761:
7756:
7751:
7747:
7743:
7739:
7735:
7731:
7724:
7716:
7712:
7707:
7702:
7698:
7694:
7690:
7686:
7682:
7678:
7674:
7667:
7659:
7655:
7651:
7647:
7643:
7639:
7635:
7631:
7624:
7616:
7612:
7608:
7604:
7600:
7596:
7589:
7581:
7577:
7573:
7569:
7565:
7561:
7557:
7553:
7549:
7545:
7544:Psychometrika
7538:
7530:
7526:
7522:
7516:
7512:
7508:
7504:
7497:
7489:
7485:
7481:
7477:
7470:
7462:
7458:
7453:
7448:
7444:
7440:
7436:
7432:
7428:
7421:
7413:
7406:
7398:
7394:
7390:
7386:
7379:
7371:
7367:
7363:
7359:
7355:
7351:
7344:
7336:
7329:
7321:
7317:
7313:
7309:
7305:
7301:
7294:
7286:
7282:
7278:
7274:
7269:
7264:
7260:
7256:
7255:
7247:
7239:
7235:
7231:
7227:
7220:
7212:
7209:
7205:
7201:
7194:
7190:
7189:Zimek, Arthur
7186:
7179:
7177:
7168:
7164:
7160:
7156:
7149:
7141:
7137:
7133:
7129:
7122:
7114:
7108:
7100:
7093:
7092:
7085:
7083:
7074:
7068:
7064:
7057:
7055:
7053:
7044:
7040:
7036:
7030:
7026:
7019:
7017:
7008:
7004:
7000:
6994:
6990:
6983:
6981:
6979:
6970:
6966:
6962:
6958:
6954:
6950:
6943:
6941:
6939:
6937:
6928:
6924:
6920:
6916:
6912:
6908:
6903:
6898:
6894:
6890:
6886:
6882:
6875:
6867:
6863:
6856:
6848:
6835:
6827:
6823:
6818:
6817:q-bio/0311039
6813:
6806:
6798:
6792:
6788:
6784:
6780:
6773:
6765:
6761:
6757:
6751:
6747:
6743:
6738:
6733:
6730:. p. 7.
6729:
6725:
6721:
6714:
6706:
6702:
6698:
6692:
6688:
6684:
6679:
6678:10.1.1.5.1279
6674:
6670:
6666:
6659:
6651:
6647:
6643:
6637:
6633:
6629:
6624:
6619:
6615:
6611:
6604:
6596:
6590:
6586:
6582:
6577:
6572:
6568:
6564:
6560:
6553:
6545:
6539:
6535:
6531:
6526:
6521:
6517:
6513:
6509:
6502:
6495:
6491:
6487:
6481:
6473:
6469:
6465:
6461:
6456:
6451:
6447:
6443:
6436:
6428:
6424:
6420:
6416:
6412:
6408:
6404:
6403:Zimek, Arthur
6400:
6394:
6387:
6381:
6372:
6364:
6360:
6356:
6352:
6348:
6344:
6340:
6333:
6325:
6318:
6310:
6306:
6302:
6296:
6292:
6285:
6277:
6271:
6267:
6263:
6258:
6253:
6249:
6242:
6240:
6230:
6225:
6221:
6217:
6213:
6206:
6198:
6196:1-57735-004-9
6192:
6188:
6184:
6180:
6173:
6166:
6162:
6159:
6154:
6146:
6142:
6138:
6134:
6130:
6126:
6122:
6118:
6117:Zimek, Arthur
6114:
6108:
6100:
6096:
6092:
6088:
6084:
6080:
6073:
6065:
6061:
6057:
6053:
6046:
6038:
6034:
6030:
6026:
6019:
6012:
6004:
6002:9780470749913
5998:
5994:
5987:
5979:
5975:
5970:
5965:
5960:
5955:
5951:
5947:
5943:
5935:
5928:
5924:
5919:
5911:
5907:
5903:
5899:
5895:
5891:
5884:
5882:
5880:
5878:
5876:
5874:
5865:
5861:
5857:
5853:
5846:
5838:
5834:
5828:
5820:
5816:
5812:
5808:
5804:
5800:
5793:
5779:on 2020-12-06
5778:
5774:
5770:
5763:
5759:
5749:
5746:
5744:
5741:
5739:
5736:
5734:
5731:
5729:
5726:
5724:
5721:
5720:
5712:
5709:
5707:
5704:
5702:
5699:
5698:
5690:
5687:
5685:
5682:
5680:
5677:
5675:
5672:
5669:
5666:
5665:
5657:
5654:
5652:
5649:
5647:
5644:
5642:
5639:
5637:
5634:
5632:
5629:
5627:
5624:
5622:
5619:
5617:
5614:
5612:
5609:
5607:
5604:
5603:
5589:
5586:
5585:
5581:
5578:
5577:
5573:
5570:
5569:
5565:
5563:
5560:
5559:
5555:
5551:
5549:
5546:
5545:
5541:
5538:
5537:
5528:
5525:
5522:
5520:
5517:
5513:
5511:
5508:
5505:
5503:
5500:
5499:
5490:
5488:
5485:
5482:
5478:
5476:
5473:
5470:
5468:
5465:
5462:
5460:
5457:
5453:
5451:
5448:
5445:
5443:
5440:
5437:
5433:
5429:
5426:
5422:
5420:
5417:
5413:
5411:
5408:
5407:
5404:
5396:
5393:
5390:
5387:
5383:
5379:
5376:
5373:
5369:
5365:
5362:
5361:
5358:
5350:
5345:
5342:
5339:
5335:
5331:
5327:
5323:
5319:
5315:
5311:
5309:
5306:
5305:
5296:
5293:
5289:
5286:
5283:
5279:
5275:
5273:
5270:
5269:
5266:
5258:
5256:
5253:
5250:
5247:
5243:
5240:
5236:
5232:
5228:
5227:gene families
5224:
5221:
5219:
5216:
5213:
5209:
5205:
5201:
5197:
5193:
5189:
5185:
5181:
5179:
5176:
5173:
5169:
5165:
5163:
5160:
5156:
5153:
5152:
5150:
5134:
5131:
5123:
5120:November 2016
5113:
5109:
5103:
5102:
5097:This section
5095:
5091:
5086:
5085:
5074:
5073:multimodality
5070:
5066:
5063:
5047:
5037:
5032:
5028:
5021:
5016:
5013:
5010:
5006:
5002:
4996:
4991:
4987:
4980:
4975:
4972:
4969:
4965:
4956:
4951:
4947:
4940:
4935:
4932:
4929:
4925:
4918:
4915:
4908:
4907:
4893:
4890:
4885:
4881:
4858:
4854:
4833:
4830:
4825:
4821:
4798:
4794:
4773:
4753:
4731:
4727:
4706:
4703:
4700:
4680:
4660:
4640:
4632:
4628:
4627:
4624:
4623:
4619:
4618:
4617:
4605:
4604:
4601:
4600:
4596:
4593:
4589:
4585:
4584:
4579:
4576:
4572:
4569:
4568:
4564:
4560:
4556:
4552:
4534:
4531:
4527:
4504:
4500:
4491:
4487:
4483:
4467:
4447:
4440:
4436:
4420:
4417:
4409:
4393:
4390:
4382:
4366:
4363:
4355:
4339:
4336:
4328:
4307:
4304:
4301:
4298:
4295:
4290:
4287:
4281:
4275:
4272:
4269:
4266:
4263:
4258:
4255:
4247:
4244:
4241:
4234:
4233:
4230:
4229:
4226:
4225:
4221:
4220:
4200:
4197:
4194:
4191:
4188:
4185:
4182:
4179:
4176:
4171:
4168:
4165:
4159:
4156:
4153:
4150:
4143:
4142:
4128:
4125:
4105:
4102:
4094:
4093:
4090:
4089:
4085:
4084:
4068:
4065:
4057:
4054:
4035:
4032:
4029:
4026:
4023:
4020:
4017:
4014:
4009:
4006:
4000:
3989:
3986:
3983:
3968:
3965:
3962:
3951:
3945:
3942:
3939:
3933:
3926:
3925:
3922:
3921:Jaccard index
3918:
3917:
3914:
3913:
3912:Jaccard index
3909:
3908:
3892:
3889:
3881:
3866:
3846:
3843:
3840:
3832:
3816:
3813:
3808:
3804:
3783:
3780:
3777:
3769:
3750:
3747:
3744:
3741:
3736:
3732:
3726:
3723:
3720:
3717:
3711:
3708:
3703:
3699:
3689:
3684:
3680:
3672:
3671:
3669:
3653:
3645:
3629:
3621:
3602:
3599:
3596:
3593:
3590:
3585:
3582:
3576:
3573:
3566:
3549:
3546:
3543:
3540:
3537:
3532:
3529:
3523:
3520:
3513:
3512:
3510:
3509:
3504:
3503:
3486:
3483:
3480:
3472:
3469:by weighting
3468:
3464:
3463:
3460:
3459:
3455:
3454:
3453:
3451:
3447:
3443:
3439:
3411:
3408:
3397:
3394:
3391:
3388:
3385:
3382:
3379:
3376:
3373:
3370:
3367:
3364:
3344:
3341:
3321:
3318:
3310:
3306:
3290:
3287:
3279:
3263:
3260:
3252:
3236:
3233:
3213:
3210:
3202:
3183:
3180:
3177:
3174:
3171:
3168:
3165:
3162:
3159:
3156:
3153:
3148:
3145:
3142:
3139:
3136:
3130:
3127:
3124:
3117:
3116:
3114:
3113:
3110:
3109:
3105:
3104:
3099:
3077:
3074:
3071:
3060:
3057:
3054:
3044:
3041:
3038:
3034:
3028:
3025:
3016:
3015:
3014:
3013:
2997:
2977:
2957:
2949:
2946:
2945:
2944:
2941:
2939:
2938:pair counting
2935:
2930:
2928:
2924:
2920:
2915:
2914:gold standard
2901:
2896:
2895:
2892:
2891:
2887:
2886:
2882:
2878:
2874:
2870:
2866:
2862:
2858:
2854:
2850:
2846:
2842:
2838:
2834:
2830:
2826:
2822:
2806:
2796:
2784:
2778:
2775:
2772:
2769:
2766:
2753:
2750:
2747:
2741:
2736:
2733:
2730:
2727:
2724:
2721:
2718:
2707:
2704:
2697:
2696:
2693:
2692:
2689:
2688:
2684:
2683:
2679:
2661:
2657:
2634:
2630:
2604:
2600:
2596:
2591:
2587:
2580:
2558:
2554:
2533:
2511:
2507:
2486:
2478:
2460:
2456:
2447:
2443:
2426:
2415:
2411:
2407:
2402:
2398:
2391:
2384:
2380:
2376:
2371:
2367:
2360:
2354:
2351:
2348:
2338:
2333:
2330:
2327:
2323:
2317:
2314:
2309:
2306:
2303:
2296:
2295:
2293:
2289:
2288:
2285:
2284:
2280:
2279:
2278:
2274:
2272:
2266:
2261:
2251:
2247:
2243:
2239:
2237:
2233:
2229:
2225:
2215:
2213:
2209:
2205:
2201:
2200:
2195:
2190:
2187:
2183:
2178:
2176:
2172:
2168:
2164:
2160:
2156:
2152:
2147:
2145:
2141:
2137:
2133:
2132:
2127:
2114:
2111:
2106:
2103:
2100:
2099:
2097:
2094:
2091:
2088:
2087:
2086:
2084:
2079:
2065:
2059:
2054:
2050:
2044:
2039:
2036:
2029:
2024:
2021:
2020:
2019:
2017:
2013:
2009:
2007:
2006:EM clustering
2002:
1998:
1993:
1991:
1975:
1967:
1963:
1947:
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1934:deterministic
1931:
1926:
1910:
1905:
1894:
1889:
1886:
1885:
1884:
1881:
1876:
1874:
1873:local optimum
1870:
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1746:
1744:
1743:fuzzy c-means
1740:
1738:
1733:
1731:
1726:
1722:
1720:
1715:
1711:
1710:local optimum
1707:
1703:
1699:
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1677:
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1267:
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1248:
1244:
1236:
1233:
1225:
1222:
1218:
1210:
1207:
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1199:
1195:Density model
1191:
1188:
1184:
1180:
1172:
1169:
1165:
1157:
1154:
1150:
1142:
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1140:
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1024:
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1012:
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1000:
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995:data analysis
993:
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788:
786:
783:
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778:
777:
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770:
763:
760:
758:
755:
753:
752:Crowdsourcing
750:
748:
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744:
738:
737:
728:
725:
724:
723:
720:
718:
715:
713:
710:
708:
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696:
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687:
684:
682:
681:Memtransistor
679:
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669:
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591:
589:
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584:
581:
579:
576:
575:
574:
571:
569:
566:
564:
563:Deep learning
561:
559:
556:
555:
552:
547:
546:
539:
536:
534:
531:
529:
527:
523:
521:
518:
517:
514:
509:
508:
499:
498:Hidden Markov
496:
494:
491:
489:
486:
485:
484:
481:
480:
477:
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464:
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459:
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330:
327:
325:
322:
320:
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312:
310:
307:
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303:
299:
295:
294:Random forest
292:
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212:
210:
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205:
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197:
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192:
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187:
185:
182:
180:
179:Data cleaning
177:
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162:
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127:
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119:
117:
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109:
107:
104:
102:
99:
97:
94:
92:
91:Meta-learning
89:
87:
84:
82:
79:
77:
74:
72:
69:
67:
64:
63:
57:
56:
53:
48:
45:
44:
40:
39:
32:
19:
11238:
11226:
11207:
11200:
11112:Econometrics
11062: /
11045:Chemometrics
11022:Epidemiology
11015: /
10988:Applications
10830:ARIMA model
10777:Q-statistic
10726:Stationarity
10654:
10622:Multivariate
10565: /
10561: /
10559:Multivariate
10557: /
10497: /
10493: /
10267:Bayes factor
10166:Signed rank
10078:
10052:
10044:
10032:
9727:Completeness
9563:Cohort study
9461:Opinion poll
9396:Missing data
9383:Study design
9338:Scatter plot
9260:Scatter plot
9253:Spearman's Ï
9215:Grouped data
8756:Hugging Face
8720:David Silver
8368:Audioâvisual
8222:Applications
8201:Augmentation
8116:
8046:
7978:(6): 56â62.
7975:
7971:
7965:
7924:
7920:
7907:
7888:
7884:
7874:
7868:: 1571â1576.
7865:
7861:
7855:
7832:cite journal
7821:
7810:
7785:
7781:
7737:
7733:
7723:
7680:
7676:
7666:
7633:
7629:
7623:
7598:
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7547:
7543:
7537:
7502:
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7479:
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7469:
7452:11441/132081
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7384:
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7353:
7349:
7343:
7334:
7328:
7303:
7299:
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7258:
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7246:
7229:
7219:
7203:
7200:Dy, Jennifer
7158:
7154:
7148:
7131:
7127:
7121:
7090:
7062:
7027:. Springer.
7024:
6988:
6952:
6948:
6884:
6880:
6874:
6865:
6855:
6834:cite journal
6805:
6778:
6772:
6727:
6713:
6668:
6658:
6613:
6603:
6566:
6552:
6515:
6501:
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6480:
6445:
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6435:
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6393:
6380:
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6346:
6342:
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6332:
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6317:
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6247:
6215:
6205:
6182:
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6128:
6124:
6107:
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6078:
6072:
6055:
6051:
6045:
6028:
6024:
6011:
5992:
5986:
5949:
5945:
5934:
5918:
5896:(1): 65â75.
5893:
5889:
5855:
5851:
5845:
5836:
5827:
5802:
5798:
5792:
5781:. Retrieved
5777:the original
5772:
5762:
5587:Geochemistry
5515:effectively.
5415:maintenance.
5126:
5117:
5106:Please help
5101:verification
5098:
5080:Applications
5068:
4620:
4614:
4597:
4581:
4570:
4551:Informedness
4222:
4086:
3910:
3506:
3500:
3456:
3435:
3308:
3106:
2947:
2942:
2937:
2931:
2910:
2899:
2888:
2880:
2876:
2872:
2864:
2860:
2856:
2852:
2848:
2844:
2840:
2836:
2832:
2828:
2824:
2685:
2546:to centroid
2445:
2281:
2275:
2270:
2267:
2263:
2248:
2244:
2240:
2235:
2231:
2227:
2223:
2221:
2197:
2191:
2179:
2148:
2129:
2125:
2123:
2075:
2010:
1994:
1927:
1923:
1877:
1862:
1846:
1835:
1821:
1817:
1815:
1808:
1793:
1779:
1762:
1757:
1749:
1747:
1736:
1729:
1723:), choosing
1718:
1713:
1704:" (although
1701:
1691:
1686:
1680:
1675:
1673:
1495:
1472:
1457:
1455:
1440:
1432:
1409:
1399:
1393:
1389:
1381:
1368:
1358:
1353:
1341:
1333:
1323:
1317:
1297:unsupervised
1290:Neural model
1287:
1270:signed graph
1261:
1237:
1226:
1221:biclustering
1211:
1192:
1185:used by the
1173:
1158:
1143:
1137:
1120:Robert Tryon
1118:in 1938 and
1116:Joseph Zubin
1113:
1106:
1102:
1081:
1075:
1068:
1064:
1062:
1027:
979:
975:
971:
970:
838:PAC learning
525:
374:
369:Hierarchical
351:
301:
255:
249:
158:
11267:Data mining
11240:WikiProject
11155:Cartography
11117:Jurimetrics
11069:Reliability
10800:Time domain
10779:(LjungâBox)
10701:Time-series
10579:Categorical
10563:Time-series
10555:Categorical
10490:(Bernoulli)
10325:Correlation
10305:Correlation
10101:JarqueâBera
10073:Chi-squared
9835:M-estimator
9788:Asymptotics
9732:Sufficiency
9499:Interaction
9411:Replication
9391:Effect size
9348:Violin plot
9328:Radar chart
9308:Forest plot
9298:Correlogram
9248:Kendall's Ï
8904:Categories
8852:Autoencoder
8807:Transformer
8675:Alex Graves
8623:OpenAI Five
8527:IBM Watsonx
8149:Convolution
8127:Overfitting
7356:(1): 1985.
5969:10481/84538
5562:Climatology
5372:communities
5172:phylogenies
2479:of cluster
2171:correlation
2081:clustering
1849:overfitting
1820:-means and
1229:Group model
992:statistical
982:) are more
722:Multi-agent
659:Transformer
558:Autoencoder
314:Naive Bayes
52:data mining
11256:Categories
11107:Demography
10825:ARMA model
10630:Regression
10207:(Friedman)
10168:(Wilcoxon)
10106:Normality
10096:Lilliefors
10043:Student's
9919:Resampling
9793:Robustness
9781:divergence
9771:Efficiency
9709:(monotone)
9704:Likelihood
9621:Population
9454:Stratified
9406:Population
9225:Dependence
9181:Count data
9112:Percentile
9089:Dispersion
9022:Arithmetic
8957:Statistics
8893:Technology
8746:EleutherAI
8705:Fei-Fei Li
8700:Yann LeCun
8613:Q-learning
8596:Decisional
8522:IBM Watson
8430:Midjourney
8322:TensorFlow
8169:Activation
8122:Regression
8117:Clustering
7734:NeuroImage
7268:1704.01036
7134:: 95â104.
6309:1110589522
6187:AAAI Press
5952:: 115265.
5783:2019-02-18
5754:References
5526:Typologies
5318:population
5246:genotyping
5202:(ESTs) or
5147:See also:
4555:Markedness
4088:Dice index
4058:Note that
3438:Rand index
3108:Rand index
2687:Dunn index
2258:See also:
2012:Mean-shift
1464:dendrogram
1423:Algorithms
1134:Definition
1082:botryology
1069:automatic
1065:clustering
976:clustering
707:Q-learning
605:Restricted
403:Mean shift
352:Clustering
329:Perceptron
257:regression
159:Clustering
154:Regression
10488:Logistic
10255:posterior
10181:Rank sum
9929:Jackknife
9924:Bootstrap
9742:Bootstrap
9677:Parameter
9626:Statistic
9421:Statistic
9333:Run chart
9318:Pie chart
9313:Histogram
9303:Fan chart
9278:Bar chart
9160:L-moments
9047:Geometric
8776:MIT CSAIL
8741:Anthropic
8710:Andrew Ng
8608:AlphaZero
8452:VideoPoet
8415:AlphaFold
8352:MindSpore
8306:SpiNNaker
8301:Memristor
8208:Diffusion
8184:Rectifier
8164:Batchnorm
8144:Attention
8139:Adversary
7992:0015-198X
7697:1088-9051
7650:0022-2836
7615:0020-0190
7564:1860-0980
7370:189915041
7226:Zimek, A.
7161:: 53â65.
7043:803401334
7007:915286380
6897:CiteSeerX
6732:CiteSeerX
6724:Zimek, A.
6673:CiteSeerX
6665:Zimek, A.
6618:CiteSeerX
6610:Zimek, A.
6571:CiteSeerX
6563:Zimek, A.
6520:CiteSeerX
6512:Zimek, A.
6450:CiteSeerX
6252:CiteSeerX
6224:CiteSeerX
6220:ACM Press
5978:0165-1781
5819:0096-851X
5326:customers
5322:consumers
5291:activity.
5278:PET scans
5248:platforms
5007:∑
4966:∑
4926:∑
4891:∈
4831:∈
4704:≪
4571:Chi Index
4486:precision
4482:G-measure
4435:precision
4282:⋅
4232:formula:
3987:∪
3966:∩
3867:β
3841:β
3778:β
3742:⋅
3733:β
3724:⋅
3718:⋅
3700:β
3685:β
3646:rate and
3644:precision
3502:precision
3484:≥
3481:β
3458:F-measure
3075:∩
3058:∈
3042:∈
3035:∑
2919:anomalies
2869:centroids
2789:′
2776:≤
2770:≤
2734:≤
2722:≤
2508:σ
2381:σ
2368:σ
2352:≠
2324:∑
2083:algorithm
1976:ε
1948:ε
1567:−
1034:distances
1030:algorithm
866:ECML PKDD
848:VC theory
795:ROC curve
727:Self-play
647:DeepDream
488:Bayes net
279:Ensembles
60:Paradigms
11202:Category
10895:Survival
10772:Johansen
10495:Binomial
10450:Isotonic
10037:(normal)
9682:location
9489:Blocking
9444:Sampling
9323:QâQ plot
9288:Box plot
9270:Graphics
9165:Skewness
9155:Kurtosis
9127:Variance
9057:Heronian
9052:Harmonic
8884:Portals
8643:Auto-GPT
8475:Word2vec
8279:Hardware
8196:Datasets
8098:Concepts
7957:22655306
7949:19076414
7764:20933091
7715:11435409
7658:11743721
7529:36701919
7461:93003939
7437:: 1â17.
7202:(eds.).
7191:(2010).
7107:citation
6919:17218491
6866:Wcci Cec
6448:: 5â33.
6363:11323096
6161:Archived
6145:36920706
6119:(2011).
6099:10833328
5929:20:181â7
5835:(1939).
5595:See also
5455:cluster.
5265:Medicine
5212:genomics
3440:is that
3309:pairwise
2936:), such
2477:centroid
2236:indirect
2228:external
2224:internal
2136:big data
1739:-means++
1721:-medoids
1377:outliers
1264:: Every
1050:data set
289:Boosting
138:Problems
11228:Commons
11175:Kriging
11060:Process
11017:studies
10876:Wavelet
10709:General
9876:Plug-in
9670:L space
9449:Cluster
9150:Moments
8968:Outline
8766:Meta AI
8603:AlphaGo
8587:PanGu-ÎŁ
8557:ChatGPT
8532:Granite
8480:Seq2seq
8459:Whisper
8380:WaveNet
8375:AlexNet
8347:Flux.jl
8327:PyTorch
8179:Sigmoid
8174:Softmax
8039:General
7929:Bibcode
7802:1775181
7755:3008313
7572:5234703
7320:2288117
7285:2284239
6969:6935380
6927:6502291
6889:Bibcode
6881:Science
6868:. IEEE.
6822:Bibcode
6764:1554722
6705:6411037
6650:2679909
6472:9289572
6427:7241355
5910:7329935
5571:Finance
5425:digital
5314:surveys
5196:pathway
5192:enzymes
5162:ecology
5069:uniform
4329:where
3666:is the
3642:is the
3203:where
2475:is the
2126:CLARANS
1992:index.
1725:medians
1694:NP-hard
1388:(also:
1340:(also:
1302:is the
1124:Cattell
984:similar
980:cluster
871:NeurIPS
688:(ECRAM)
642:AlexNet
284:Bagging
11097:Census
10687:Normal
10635:Manova
10455:Robust
10205:2-way
10197:1-way
10035:-test
9706:
9283:Biplot
9074:Median
9067:Lehmer
9009:Center
8781:Huawei
8761:OpenAI
8663:People
8633:MuZero
8495:Gemini
8490:Claude
8425:DALL-E
8337:Theano
7990:
7955:
7947:
7800:
7762:
7752:
7713:
7706:311112
7703:
7695:
7656:
7648:
7613:
7580:930698
7578:
7570:
7562:
7527:
7517:
7459:
7368:
7318:
7283:
7211:SIGKDD
7101:, 2017
7069:
7041:
7031:
7005:
6995:
6967:
6925:
6917:
6899:
6793:
6762:
6752:
6734:
6703:
6693:
6675:
6648:
6638:
6620:
6591:
6573:
6540:
6522:
6492:. In:
6470:
6452:
6425:
6361:
6307:
6297:
6272:
6254:
6226:
6193:
6143:
6097:
5999:
5976:
5908:
5817:
5534:Others
5487:DevOps
5395:Flickr
5386:Clusty
5382:Google
5282:tissue
5233:, and
5159:animal
4586:is an
4490:recall
4439:recall
4410:. The
4383:, and
3831:recall
3668:recall
3622:where
3508:recall
3499:. Let
3471:recall
3280:, and
2948:Purity
2843:, and
2823:where
2573:, and
2444:where
2232:manual
2186:OPTICS
2182:DBSCAN
2175:SUBCLU
2128:, and
2064:OPTICS
2049:DBSCAN
2035:DBSCAN
2001:OPTICS
1997:DBSCAN
1990:R-tree
1938:OPTICS
1930:DBSCAN
1272:has a
1247:clique
1206:OPTICS
1202:DBSCAN
1105:, and
1090:ÎČÏÏÏÏ
Ï
1084:(from
664:Vision
520:RANSAC
398:OPTICS
393:DBSCAN
377:-means
184:AutoML
10721:Trend
10250:prior
10192:anova
10081:-test
10055:-test
10047:-test
9954:Power
9899:Pivot
9692:shape
9687:scale
9137:Shape
9117:Range
9062:Heinz
9037:Cubic
8973:Index
8847:Mamba
8618:SARSA
8582:LLaMA
8577:BLOOM
8562:GPT-J
8552:GPT-4
8547:GPT-3
8542:GPT-2
8537:GPT-1
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