CHAPTER 7 - NONHIERARCHICAL CLUSTERING METHODS

Abstract

The non-hierarchical clustering methods are designed to cluster data units into a single classification of k clusters, where k either is specified a priori or is determined as part of the clustering method. The idea in most of these methods is to choose some initial partition of the data units and then alter cluster memberships to obtain a better partition. The various algorithms that have been proposed differ as to what constitutes a “better partition” and the methods that might be used for achieving improvements. The non-hierarchical clustering methods might be used with larger problems than the hierarchical methods because it is not necessary to calculate and store the similarity matrix; it is not necessary to store the data set. The data units are processed serially and can be read from tape or disk, as needed. This characteristic makes it possible to cluster arbitrarily large collections of data units.