The aim of cluster analysis is to search for patterns in a dataset by grouping similar items. The number of groups (clusters) need not be fixed in advance, and, in fact, there is an array of different methods, which share a common need: the definition of a distance between observations, which is used to measure similarity or dissimilarity. Observations within a cluster should be similar one to another and dissimilar from items in other clusters. We first outline a few methods to measure distance, and then we describe the two main families of clustering methods, hierarchical and nonhierarchical.
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