Simplifying and improving swarm-based clustering

Abstract

Swarm-based clustering has enthused researchers for its ability to find clusters in datasets automatically, and without requiring users to specify the number of clusters. While conventional wisdom suggests that swarm intelligence contributes to this ability, recent works have provided alternative explanation about underlying stochastic heuristics that are really at work. This paper shows that the working principles of several recent SBC methods can be explained using a stochastic clustering framework that is unrelated to swarm intelligence. The framework is theoretically simple and in practice easy to implement. We also incorporate a mechanism to calibrate a key parameter so as to enhance the clustering performance. Despite the simplicity of the enhanced algorithm, experimental results show that it outperforms two recent SBC methods in terms of clustering accuracy and efficiency in the majority of the datasets used in this study.