Hierarchical division clustering framework for categorical data

Although many divisive hierarchical clustering methods for processing categorical data have been presented in the literature, none have been systematically or comprehensively investigated. This paper presents a systematic analysis of existing methods, with respective advantages and disadvantages summarized to develop a unified divisive hierarchical clustering framework that follows three general steps: (1) select attributes for splitting a selected cluster; (2) based on these attributes, generate bipartitions of the cluster; and (3) determine which of the resulting clusters should be further split. Using the proposed framework, representative existing algorithms are compared, and better-performing algorithms are produced through improvements relevant to each step of the unified framework. Experimental results on fifteen UCI benchmark datasets reveal that application of the proposed framework significantly improves the clustering performance of a number of algorithms relative to baseline.