On Simulating Skewed and Cluster-Weighted Data for Studying Performance of Clustering Algorithms

In this article, extensions to the recently introduced concept of pairwise overlap between mixture components are proposed. The notion of overlap is useful for studying the systematic performance of clustering algorithms. Existing methods can be used for simulating elliptical data according to pre-specified overlap characteristics. First, an approach to simulating skewed clusters with a desired overlap is proposed. Next, an extension to measuring overlap in cluster-weighted models is considered. Thus, this article provides important extensions to the existing methods for simulating heterogeneous data for studying the systematic performance of clustering algorithms. Supplementary materials for this article are available online.