Cluster analysis is the process of grouping "similar" objects into groups known as "clusters", along with the analysis of these results.
Cluster analysis is the task of grouping objects into subsets (called clusters) so that observations in the same cluster are similar in some sense, while observations in different clusters are dissimilar.
In machine-learning and data-mining, clustering is a method of unsupervised learning used to discover hidden structure in unlabeled data, and is commonly used in exploratory data analysis. Popular algorithms include k-means, expectation maximization (EM), spectral clustering, correlation clustering and hierarchical-clustering.
Related topics: classification, pattern-recognition, knowledge discovery, taxonomy. Not to be confused with cluster computing.
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