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