An artificial bee colony algorithm for mixture model-based clustering

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.