Empirical comparison of a monothetic divisive clustering method with the Ward and the k-means clustering methods

DIVCLUS-T is a descendant hierarchical clustering methods based on the same monothetic approach than segmentation but from an unsupervised point of view. The dendrogram of the hierarchy is easy to interpret and can be read as decision tree. We present DIVCLUS-T on a small numerical and a small categorical example. DIVCLUS-T is then compared with two polythetic clustering methods: the Ward ascendant hierarchical clustering method and the k-means partitional method. The three algoritms are applied and compared on six databases of the UCI Machine Learning repository