Nonparametric Information Theoretic Clustering Algorithm

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.

• Type: Conference
• Year: 2010
• Where: ICML
• Authors: Lev Faivishevsky, Jacob Goldberger
• Keywords: Average Cluster Entropies, Data Points, ICML 2010, Machine Learning, Mutual Information