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. © 2011 Springer-Verlag.
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CITATION STYLE
Georgieva, O., Tschumitschew, K., & Klawonn, F. (2011). Cluster validity measures based on the minimum description length principle. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6881 LNAI, pp. 82–89). https://doi.org/10.1007/978-3-642-23851-2_9
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