A comparison between K-means and Support Vector Clustering for Categorical Data

Standard clustering methods fail when data are characterized by non-linear associations. A suitable solution consists in mapping data in a higher dimensional feature space where clusters are separable. The aim of the present contribution is to propose a new technique in this context and to compare it with k-means technique.