基于互信息和分形维数相结合的选择性聚类融合算法研究<sup>*</sup>

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Abstract Traditional clustering ensemble algorithm can not eliminate the influence of inferior quality clustering members and is also characterized with lower clustering accuracy. To solve the problems, a selective clustering ensemble algorithm based on fractal dimension is proposed. Firstly, the proposed algorithm is used to realize incremental clustering and can find arbitrary shape clustering. Then, according to the selection strategy of weight values based on normalized mutual information, the proposed algorithm selects high quality clustering members to realize integration by using weighted co-association matrix and get the final clustering results. The experimental results show that compared to the traditional clustering ensemble algorithm, the proposed algorithm improves the clustering quality and has good extensibility.

Key words： Selective Clustering Ensemble, Fractal Dimension, Normalized Mutual Information Selection Strategy, Co-association matrix

Received: 11 July 2013

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	Articles by authors
	WU Xiao-Xuan
	NI Zhi-Wei
	NI Li-Ping
	ZHANG Chen

Cite this article:

WU Xiao-Xuan,NI Zhi-Wei,NI Li-Ping等. Research on Selective Clustering Ensemble Algorithm Based onNormalized Mutual Information and Fractal Dimension[J]. , 2014, 27(9): 847-855.

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http://manu46.magtech.com.cn/Jweb_prai/EN/ OR http://manu46.magtech.com.cn/Jweb_prai/EN/Y2014/V27/I9/847

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