A Probabilistic Approach to Latent Cluster Analysis

摘要

Facing a large number of clustering solutions,cluster ensemble method provides an effective approach to aggregating them into a better one.In this paper,we propose a novel cluster ensemble method from probabilistic perspective.It assumes that each clustering solution is generated from a latent cluster model,under the control of two probabilistic parameters.Thus,the cluster ensemble problem is reformulated into an optimization problem of maximum likelihood.An EM-style algorithm is designed to solve this problem.It can determine the number of clusters automatically.Experimenal results have shown that the proposed algorithm outperforms the state-of-the-art methods including EAC-AL,CSPA,HGPA,and MCLA.Furthermore,it has been shown that our algorithm is stable in the predicted numbers of clusters.