Cluster Analysis

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Cluster analysis, often called clustering, is a method used to organize large sets of unlabeled data into groups based on similarity. Each group, or cluster, contains items that are more alike to each other than to items in other clusters. It’s not a single technique but a collection of approaches that use different ways to measure similarity. Common algorithms include k-means, which divides data into a set number of clusters, hierarchical clustering, which builds a tree-like structure of groups, and DBSCAN, which finds clusters of varying shapes based on data density.

Clustering is widely used in areas like identifying customer segments, grouping documents, analyzing genetic data, or organizing images. Because clustering doesn’t rely on predefined labels, evaluating results is more about how consistent, stable, or useful the groups are for real tasks.

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