Finding number of clusters in single-step with similarity-based information-theoretic algorithm

Abstract

A single-step algorithm is presented to find the number of clusters in a dataset. An almost two-valued function called cluster-boundary indicator is introduced with the use of similarity-based information-theoretic sample entropy and probability descriptions. This function finds inter-cluster boundary samples for cluster availability in a single iteration. Experiments with synthetic and anonymous real datasets show that the new algorithm outperforms its major counterparts statistically in terms of time complexity and the number of clusters found successfully.