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Clustering is an unsupervised Machine Learning task where elements of a set must be divided in groups, called clusters, such that elements in the same group are more similar (according to some specific way defined by the used algorithm) to each other then to those in other clusters.

Examples of clustering are: K-Means (where K is the number of clusters and it is defined in advance regardless of the input data) and density-based clustering, such as DBSCAN and OPTICS, where the number of clusters is not fixed in advance and it depends on the density of the input data.