Empirical Comparison of a Monothetic Divisive Clustering Method with the Ward and the k-means Clustering Methods

Abstract

DIVCLUS-T is a descendant hierarchical clustering method based on the same monothetic approach than classification and regression trees but from an unsupervised point of view. The aim is not to predict a continuous variable (re-gression) or a categorical variable (classification) but to construct a hierarchy. The dendrogram of the hierarchy is easy to interpret and can be read as decision tree. An example of this new type of dendrogram is given on a small categorical dataset. DIVCLUS-T is then compared empirically with two polythetic clustering methods: the Ward ascendant hierarchical clustering method and the k-means partitional method. The three algorithms are applied and compared on six databases of the UCI Machine Learning repository.