Maximum certainty data partitioning

Problems in data analysis often require the unsupervised partitioning of a dataset into clusters. Many methods exist for such partitioning but most ha ve the weakness of being model-based (most assuming hyper-ellipsoidal clust ers) or computationally infeasible in anything more than a three-dimensiona l data space. We re-consider the notion of cluster analysis in information- theoretic terms and show that minimisation of partition entropy can be used to estimate the number and structure of probable data generators. (C) 2000 Pattern Recognition Society. Published by Elsevier Science Ltd. All rights reserved.