Cluster Analysis: Overview
Gene R. Lowrimore, Kenneth G. Mantón · Wiley StatsRef: Statistics Reference Online · 2016
Abstract This article discusses analysis techniques for clustering objects into hopefully meaningful sets. Hierarchical methods are presented for clustering both variables and cases. Examples are presented for variations on the methods. A nonhierarchical method,k‐means, is discussed for continuous data. An example comparing the results ofk‐means clustering to an ad hoc method using principal components analysis is given. Finally, an example is given usingk‐means clustering in conjunction with a grade of membership fuzzy clustering.