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Cluster analysis - Wikipedia The notion of a "cluster" cannot be precisely defined, which is one of the reasons why there are so many clustering algorithms [5] 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 algorithms can be given The notion of a cluster, as found by different algorithms, varies
Automatic clustering algorithms - Wikipedia Automatic clustering algorithms are algorithms that can perform clustering without prior knowledge of data sets In contrast with other clustering techniques, automatic clustering algorithms can determine the optimal number of clusters even in the presence of noise and outliers
List of text mining methods - Wikipedia Centroid-based Clustering: Unsupervised learning method Clusters are determined based on data points [1] Fast Global K -Means: Made to accelerate Global K -Means [2] Global K -Means: Global K -Means is an algorithm that begins with one cluster, and then divides into multiple clusters based on the number required [2] K -Means: An algorithm that requires two parameters: K, a number of
Hierarchical clustering - Wikipedia The standard algorithm for hierarchical agglomerative clustering (HAC) has a time complexity of and requires memory, which makes it too slow for even medium data sets However, for some special cases, optimal efficient agglomerative methods (of complexity ) are known: SLINK[4] for single-linkage and CLINK [5] for complete-linkage clustering
Fuzzy clustering - Wikipedia Fuzzy clustering (also referred to as soft clustering or soft k-means) is a form of clustering in which each data point can belong to more than one cluster Clustering or cluster analysis involves assigning data points to clusters such that items in the same cluster are as similar as possible, while items belonging to different clusters are as
k-means clustering - Wikipedia Cluster analysis, a fundamental task in data mining and machine learning, involves grouping a set of data points into clusters based on their similarity k -means clustering is a popular algorithm used for partitioning data into k clusters, where each cluster is represented by its centroid
Single-linkage clustering - Wikipedia The following algorithm is an agglomerative scheme that erases rows and columns in a proximity matrix as old clusters are merged into new ones The proximity matrix contains all distances The clusterings are assigned sequence numbers and is the level of the -th clustering A cluster with sequence number m is denoted (m) and the proximity between clusters and is denoted The single linkage
Category:Cluster analysis algorithms - Wikipedia This category contains algorithms used for cluster analysis Pages in category "Cluster analysis algorithms" The following 42 pages are in this category, out of 42 total This list may not reflect recent changes
Determining the number of clusters in a data set - Wikipedia For a certain class of clustering algorithms (in particular k -means, k -medoids and expectation–maximization algorithm), there is a parameter commonly referred to as k that specifies the number of clusters to detect
Complete-linkage clustering - Wikipedia Complete-linkage clustering is one of several methods of agglomerative hierarchical clustering At the beginning of the process, each element is in a cluster of its own