Clustering under composite generative models

Tiexing Wang, Donald J. Bucci, Yingbin Liang, Biao Chen, Pramod K. Varshney

Abstract: This paper studies clustering of data samples generated from composite distributions using the Kolmogorov-Smirnov (KS) based K-means algorithm. All data sequences are assumed to be generated from unknown continuous distributions. The maximum intra-cluster KS distance of each distribution cluster is assumed to be smaller than the minimum inter-cluster KS distance of different clusters. The analysis of convergence and upper bounds on the error probability are provided for both cases with known and unknown number of clusters. Furthermore, it is shown that the probability of error decays exponentially as the number of samples in each data sequence goes to infinity, and the error exponent is only a function of the difference of the inter-cluster and intra-cluster KS distances. The analysis is validated by simulation results.