Cluster validity measures based on the minimum description length principle

Authors:
Olga Georgieva;Katharina Tschumitschew;Frank Klawonn
Affiliations:
Department of Software Engineering, Faculty of Mathematics and Informatics, Sofia University, Sofia, Bulgaria;Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbuettel, Germany;Department of Computer Science, Ostfalia University of Applied Sciences, Wolfenbuettel, Germany and Bioinformatics and Statistics, Helmholtz Centre for Infection Research, Braunschweig, Germany
Venue:
KES'11 Proceedings of the 15th international conference on Knowledge-based and intelligent information and engineering systems - Volume Part I
Year:
2011

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Abstract

Determining the number of clusters is a crucial problem in cluster analysis. Cluster validity measures are one way to try to find the optimum number of clusters, especially for prototype-based clustering. However, no validity measure turns out to work well in all cases. In this paper, we propose an approach to determine the number of cluster based on the minimum description length principle which does not need high computational costs and is also applicable in the context of fuzzy clustering.