A novel approach to clustering co-occurrence data poses it as an optimization problem in information theory which minimizes the resulting loss in mutual information. A divisive cl...
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 d...
Hierarchical clustering methods are widely used in various scientific domains such as molecular biology, medicine, economy, etc. Despite the maturity of the research field of hie...
— Recent work has revealed a close connection between certain information theoretic divergence measures and properties of Mercer kernel feature spaces. Specifically, it has been...
Abstract. This paper presents a probabilistic model for combining cluster ensembles utilizing information theoretic measures. Starting from a co-association matrix which summarizes...