¹ Department of Mathematics and Computational Science, Guang Dong University of Business Studies, Guangzhou, China, 510320, Email: lcs5812084@sina.com
² College of Electrical and Information Engineering, Hunan University, Changsha, China, 410083
³ College of Automation Science and Engineering, South China University of Technology, Guangzhou, China, 510635

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Abstract—this paper firstly generalizes majority vote to fuzzy majority vote, then proposes a cluster matching algorithm that is able to establish correspondence among fuzzy clusters from different fuzzy partitions over a common data set. Finally a new combination model of fuzzy partitions is build on the basis of the proposed cluster matching algorithm and fuzzy majority vote. Comparative results show that the proposed combination model is able to foster strengths and circumvent weaknesses of component fuzzy partitions and to combine the component fuzzy partitions into a better fuzzy partition than any of component fuzzy partitions and those resulted from two current combination models of fuzzy partitions.

Index Terms—fuzzy vote, fuzzy majority vote, combination of fuzzy partitions, evaluation of fuzzy partition

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Cite: Chun sheng Li, Yaonan Wang, and Haidong Yang, " Combining Fuzzy Partitions Using Fuzzy Majority Vote and KNN," Journal of Computers vol. 5, no. 5, pp. 791-798, 2010.