Detecting the number of clusters using a Support Vector Machine approach

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 of clusters. 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. © Springer-Verlag Berlin Heidelberg 2002.