Paper Title:
A Stochastic Disturbance of Particle Swarm Optimization for K-Means Clustering Method
  Abstract

This paper presents a hybrid-clustering algorithm that is a stochastic disturbance of particle swarm optimization (PSO) for K-means clustering method (SDPSO-K). The proposed algorithm can improve the particle global searching ability in PSO to avoid the K-means disadvantage of being easily trapped in a local optimal solution and to save the expensive computational cost of PSO clustering. The performance of the SDPSO-K, compared with three recently developed modified PSO techniques and related clustering algorithms for six datasets, indicates that the SDPSO-K algorithm is clearly and consistently superior in terms of precision and robustness.

  Info
Periodical
Advanced Materials Research (Volumes 268-270)
Edited by
Feng Xiong
Pages
10-15
DOI
10.4028/www.scientific.net/AMR.268-270.10
Citation
J. Y. Chen, "A Stochastic Disturbance of Particle Swarm Optimization for K-Means Clustering Method", Advanced Materials Research, Vols. 268-270, pp. 10-15, 2011
Online since
July 2011
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