An artificial bee colony algorithm for mixture model-based clustering

Abstract

Finite mixture model-based clustering is a popular method to perform unsupervised learning, however the classic approach for parameter estimation, the expectation-maximization algorithm, is quite susceptible to converging to, at times extremely poor, local maxima. Recently, swarm-based algorithms have gained popularity in a number of optimization scenarios. One such swarm-based optimization procedure, the artificial bee colony, incorporates both local searching and multiple solution generating to explore the parameter space. Herein, we propose hybridizing these algorithms for the task of fitting a multivariate Gaussian mixture model. This algorithm’s performance is contrasted with both the traditional approach and another hybrid algorithm on simulated and real data.

KEYWORDS:

• Artificial bee colony
• Cluster analysis
• EM algorithm
• Finite mixture models
• Model-based clustering
• Swarm intelligence

Acknowledgments

The authors would like to thank the editorial team and the anonymous reviewers for helpful comments on an earlier version of this manuscript.

Additional information

Funding

This research was supported by the Natural Sciences and Engineering Research Council of Canada (RGPIN-2014-06370) through their Discovery Grants program. Infrastructure funding was provided by the Canada Foundation for Innovation through their John R. Evans Leaders Fund (#35578).