Clustering evaluation in feature space

Abstract

Many clustering algorithms require some parameters that often are neither a priori known nor easy to estimate, like the number of classes. Measures of clustering quality can consequently be used to a posteriori estimate these values. This paper proposes such an index of clustering evaluation that deals with kernel methods like kernel-k-means. More precisely, it presents an extension of the well-known Davies & Bouldin's index. Kernel clustering methods are particularly relevant because of their ability to deal with initially non-linearly separable clusters. The interest of the following clustering evaluation is then to get around the issue of the not explicitly known data transformation of such kernel methods. Kernel Davies & Bouldin's index is finally used to a posteriori estimate the parameters of the kernel-k-means method applied on some toys datasets and Fisher's Iris dataset. © Springer-Verlag Berlin Heidelberg 2007.