一种改进的粒子群优化快速聚类算法

doi:10.3969/j.issn.1001-2400.2012.05.011

Abstract

Abstract:

This paper presents an improved particle swarm optimization based fast K-means algorithm which effectively overcomes the shortcomings of the K-means algorithm such as sensitive to initial cluster centroid and easiness to fall into local optimum so as to affect the clustering results. Compared with the existing particle clustering algorithm, is algorithm first normalizes the attributes of all the samples, and then computes the dissimilarity matrix. We propose simplified particle encoding rules and use PSO-based K-means clustering based on the dissimilarity matrix to ensure the basis for the clustering effect and reduce computational complexity. Experimental results on several UCI data sets validate the advantages of the proposed algorithm.

Key words: PSO, clustering, K-Means, dissimilarity, fitness

CLC Number:

TP391

WANG Zonghu;LIU Zhijing;CHEN Donghui. Improved PSO-based fast clustering algorithm[J].J4, 2012, 39(5): 61-65+78.

References

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Improved PSO-based fast clustering algorithm

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