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| Fuzzy Clustering Ensemble Model Based on Distance Decision |
| FEI Bowen① QIU Yunfei② LIU Wanjun② LIU Daqian③ |
①(School of Business Administration, Liaoning Technical University, Huludao 125105, China)
②(School of Software, Liaoning Technical University, Huludao 125105, China)
③(School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China) |
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Abstract Fuzzy clustering is a kind of clustering algorithm which shows superior performance in recent years, however, the algorithm is sensitive to the initial cluster center and can not obtain accurate results of clustering for the boundary samples. In order to improve the accuracy and stability of clustering, this paper proposes a
novel approach of fuzzy clustering ensemble model based on distance decision by combining multiple fuzzy clustering results. First of all, performing several times clustering for data samples by using FCM (Fuzzy CMeans), and corresponding membership matrices are obtained. Then, a new method of distance decision is
proposed, a cumulative distance matrix is constructed by the membership matrices. Finally, the distance matrix is introduced into the Density Peaks (DP) algorithm, and the final results of clustering are obtained by using the improved DP algorithm for clustering ensemble. The results of the experiment show that the clustering ensemble model proposed in this paper is more effective than other classical clustering ensemble model on the 9 data sets in UCI machine learning database.
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Received: 15 November 2017
Published: 07 June 2018
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| Fund:The Young Scientists Fund of the National Natural Science Foundation of China (61401185) |
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Corresponding Authors:
FEI Bowen
E-mail: feibowen2098@163.com
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