A nonparametric information theoretic clustering algorithm
Lev Faivishevsky and Jacob Goldberger
In: ICML 2010, 21 June - 24 June 2010, Israel.
In this paper we propose a novel clustering algorithm based on maximizing the mutual information between data points and clusters. Unlike previous methods, we neither assume the data are given in terms of distributions nor impose any parametric model on the within-cluster distribution. Instead, we utilize a non-parametric estimation of the average cluster entropies and search for a clustering that maximizes the estimated mutual information between data points and clusters. The improved performance of the proposed
algorithm is demonstrated on several standard datasets.