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.

• Periodical: Advanced Materials Research (Volumes 268-270)
• Pages: 10-15
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.268-270.10
• Online since: July 2011
• Authors: Jun Yan Chen
• Keywords: K-Means Algorithm, Particle Swarm Optimization Clustering, Stochastic Disturbance

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