Generally, cluster analysis refers to the goal of identifying or discovering groups within the data, in which the primary caveat is that the groups are not known a priori. Prior to discussing methods for identifying clusters, it is helpful to consider the fundamental question: What is a cluster? For an N × P data matrix X, containing measurements on N observations across P variables, each observation can be thought of as a point in P dimensional space. Clusters then are groups of points in P dimensional space that are similar in some fashion. After furthering the introduction of clusters, this entry lists and then examines the seven steps of cluster analysis. Those steps include determining which observations are to be clustered, which variables are to ...
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