Research on Selective Clustering Ensemble Algorithm Based onNormalized Mutual Information and Fractal Dimension
WU Xiao-Xuan, NI Zhi-Wei, NI Li-Ping, ZHANG Chen
1.School of Management, Hefei University of Technology, Hefei 230009 2.Key Laboratory of Process Optimization and Intelligent Decision-making, Ministry of Education, Hefei 230009
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.
吴晓璇，倪志伟，倪丽萍，张琛. 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*[J]. 模式识别与人工智能, 2014, 27(9): 847-855.
WU Xiao-Xuan, NI Zhi-Wei, NI Li-Ping, ZHANG Chen. Research on Selective Clustering Ensemble Algorithm Based onNormalized Mutual Information and Fractal Dimension. , 2014, 27(9): 847-855.
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2014, Vol. 27 
