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.
|Title of host publication||2012 IEEE Congress on Evolutionary Computation, CEC 2012|
|Publication status||Published - 2012|
|Event||2012 IEEE Congress on Evolutionary Computation, CEC 2012 - Brisbane, QLD, Australia|
Duration: 10 Jun 2012 → 15 Jun 2012
|Name||2012 IEEE Congress on Evolutionary Computation, CEC 2012|
|Conference||2012 IEEE Congress on Evolutionary Computation, CEC 2012|
|Period||10/06/12 → 15/06/12|
Copyright 2012 Elsevier B.V., All rights reserved.
ASJC Scopus subject areas
- Computational Theory and Mathematics
- Theoretical Computer Science