Divisive Hierarchical Clustering
Lynne Billard, Edwin Diday · 2019
This chapter explains the divisive hierarchical clustering in detail as it pertains to symbolic data. Divisive clustering techniques are (broadly) either monothetic or polythetic methods. Monothetic methods involve one variable at a time considered successively across all variables. In contrast, polythetic methods consider all variables simultaneously. Typically, the underlying clustering criteria involve some form of distance or dissimilarity measure, and since for any set of data there can be many possible distance/dissimilarity measures, then the ensuing clusters are not necessarily unique across methods/measures. The divisive techniques use the generic “dissimilarity” measure unless it is a distance measure specifically under discussion. There are a number of other aspects needing attention for divisive hierarchy trees. A different type of divisive procedure is the class of classification and regression tress. For these methods, the construction of the hierarchy tree is formed at each stage by either a classification method or by a regression analyses.