Detecting the Number of Clusters Using a Support Vector Machine Approach

Authors:
Javier M. Moguerza;Alberto Muñoz;Manuel Martín-Merino
Affiliations:
-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 7
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Abstract

In this work we introduce a new methodology to determine the number of clusters in a data set. We use a hierarchical approach that builds upon the use of any given (user-defined) clustering algorithm to produce a decision tree that returns the number ofclusters. The decision rule takes advantage of the ability of Support Vector Machines (SVM) to detect both density gaps and high-density regions in data sets. The method has been successfuly applied on a variety of artificial and real data sets, covering a broad range of structures, group densities, data dimensionalities and number of groups.