Memetic clustering based on particle swarm optimizer and K-means
Zexuan Zhu, Wenmin Liu, Shan He, Zhen Ji · 2012
This paper proposes an efficient memetic clustering algorithm (MCA) for clustering based on particle swarm optimizer (PSO) and K-means. Particularly, PSO is used as a global search to allow fast exploration of the candidate cluster centers. PSO has strong ability to find high quality solutions within tractable time, but it suffers from slow-down convergence as the swarm approaching optima. K-means, achieving fast convergence to optimum solutions, is utilized as local search to fine-tune the solutions of PSO in the framework of memetic algorithm. The performance of MCA is evaluated on four synthetic datasets and three high-dimensional gene expression datasets. Comparison study to K-means, PSO, and PSO-KM (jointed PSO and K-means) indicates that MCA is capable of identifying cluster centers more precisely and robustly than the other counterpart algorithms by taking advantage of both PSO and K-means.