Maximum Certainty Data Partitioning

S.J. Roberts, R.M. Everson and I. Rezek

Abstract

Problems in data analysis often require the unsupervised partitioning of a data set into clusters. Many methods exist for such partitioning but most have the weakness of being model-based (most assuming hyper-ellipsoidal clusters) or computationally infeasible in anything more than a 3-dimensional 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.

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