Model-Based Hierarchical Clustering for Categorical Data

摘要

Agglomerative hierarchical clustering methods based on Gaussian probability models have recently shown to be efficient in different applications. However, the emerging of pattern recognition applications where the features are binary or integer-valued demand extending research efforts to such data types. This paper proposes a hierarchical clustering framework for clustering categorical data based on Multinomial and Bernoulli mixture models. We have compared two widely used density-based distances, namely; Bhattacharyya and KullbackLeibler. The merits of our proposed framework have been shown through extensive experiments on clustering text and images using the bag of visual words model.