Clustering Algorithm Combining CPSO with K-Means

DOI

10.2991/ameii-15.2015.140How to use a DOI?

Keywords

K-Means; Clustering; Particle Swarm Optimization; Chaotic

Abstract

A clustering algorithm combining particle swarm optimization (CPSO) with K-Means (KM-CPSO) is proposed, which features better search efficiency than K-Means, PSO and CPSO. The K-Means algorithms cannot guarantee convergence to global optima and suffer in local optimal cluster center because they are sensitive to initial cluster centers. Chaotic particle swarm optimization (CPSO) can find global optimal solution; meanwhile K-Means can achieve local optima. The CPSO-KM algorithm utilizes both global search capability of CPSO and local search capability of K-Means. CPSO-KM algorithm has been tested with two synthetic datasets and three classical data sets from UCI. Experimental results show better performance of the CPSO-KM as compared to K-Means, PSO and CPSO.

Copyright

© 2015, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Chunqin Gu
AU  - Qian Tao
PY  - 2015/04
DA  - 2015/04
TI  - Clustering Algorithm Combining CPSO with K-Means
BT  - Proceedings of the International Conference on Advances in Mechanical Engineering and Industrial Informatics
PB  - Atlantis Press
SP  - 749
EP  - 755
SN  - 2352-5401
UR  - https://doi.org/10.2991/ameii-15.2015.140
DO  - 10.2991/ameii-15.2015.140
ID  - Gu2015/04
ER  -