Clustering Algorithm Based on Improved Particle Swarm Optimization

Hong Chun Wang; Feng Wen  Wen; Feng  Song

doi:10.4028/www.scientific.net/AMR.765-767.486

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 765-767Clustering Algorithm Based on Improved Particle...

Clustering Algorithm Based on Improved Particle Swarm Optimization

Abstract:

K-means algorithm has therefore become one of the methods widely used in cluster analysis. But the classification results of K-means algorithm depend on the initial cluster centers choice. We present a new neighborhood for PSO methods called the area of influence (AOI) and consider the combination of K-means has strong capacity of local searching and PSO has power global search ability. The improved PSO, i.e., improves the K-means local searching capacity, accelerates the convergence rate, and prevents the premature convergence effectively.

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Periodical:

Advanced Materials Research (Volumes 765-767)

Pages:

486-488

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.765-767.486

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Online since:

September 2013

Authors:

Hong Chun Wang, Feng Wen Wen, Feng Song

Keywords:

Clustering Analysis, K-Mean, Particle Swarm Optimization (PSO)

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