A Novel Data Clustering Algorithm based on Modified Adaptive Particle Swarm Optimization

Fuzzy clustering is a popular unsupervised learning method used in cluster analysis which allows a point in large data sets belongs to two or more clusters. Prior work suggests that Particle Swarm Optimization based approach could be a powerful tool for solving clustering problems. In this paper, we propose a data clustering algorithm based on modified adaptive particle swarm optimization. We choose to use artificial bee colony algorithm combined with PSO technique to modify the traditional clustering methods due to its fast convergence and the presence of adaptive mechanisms based on the evolutionary factor. On the one hand, Particle Swarm Optimization is proven to be an effective and robust technique for fuzzy clustering. On the other hand, the artificial bee colony algorithm has the capability to generate diversity within the swarm when the guide bees are in the exploration mode. Through numerical analysis and experimental simulation, we verify that our algorithm performs much better compared with other state-of-the-art algorithms. Future research schedule is also discussed in the final part.