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Clustering is the unsupervised learning technique that groups a set of observations into subsets, called clusters, so that items within the same group are more similar to each other than to items in other groups. There are no labels to learn: the algorithm must discover a hidden similarity structure in the data on its own.
Similarity is usually measured with a mathematical distance, Euclidean distance being the most common, computed over the numeric features of each observation. Different algorithm families exist: centroid-based ones like k-means, hierarchical ones that build a tree of successive groupings, and density-based ones like DBSCAN, capable of finding irregularly shaped clusters and isolating points that belong to no group at all.
In applied AI, clustering segments customers for targeted marketing campaigns, groups similar documents or articles to organize large archives, detects communities in a social network, compresses datasets by grouping redundant observations, and, in computer vision, helps organize similar images without manual labels. It is often the first exploratory step when tackling a new dataset that is not yet well understood.
The term cluster entered the statistical vocabulary in the mid-twentieth century as researchers began formalizing methods for grouping similar objects in taxonomy, psychology and biology. The expression cluster analysis appears in the scientific literature as early as the 1930s and spread widely in the 1960s with the growth of computational multivariate statistics, well before it became a standard machine learning tool.
Grace's weekly leagues group users with similar skill levels into homogeneous tiers, a principle that closely resembles clustering: similar elements gathered in the same group for balanced competition.
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