Fuzzy Clustering Ensemble Model Based on Distance Decision

Bowen FEI; Yunfei QIU; Wanjun LIU; Daqian LIU

doi:10.11999/JEIT171065

Volume 40 Issue 8

Aug. 2018

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Article Navigation > Journal of Electronics & Information Technology > 2018 > 40(8): 1895-1903

Bowen FEI, Yunfei QIU, Wanjun LIU, Daqian LIU. Fuzzy Clustering Ensemble Model Based on Distance Decision[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1895-1903. doi: 10.11999/JEIT171065

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Bowen FEI, Yunfei QIU, Wanjun LIU, Daqian LIU. Fuzzy Clustering Ensemble Model Based on Distance Decision[J]. Journal of Electronics & Information Technology, 2018, 40(8): 1895-1903. doi: 10.11999/JEIT171065

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Fuzzy Clustering Ensemble Model Based on Distance Decision

doi: 10.11999/JEIT171065

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

Funds: The Young Scientists Fund of the National Natural Science Foundation of China (61401185)

Received Date: 2017-11-15
Rev Recd Date: 2018-05-09

Available Online: 2018-06-07

Publish Date: 2018-08-01

Abstract

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 C-Means), 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.
- Fuzzy clustering,
- Ensemble model,
- Distance decision,
- Membership matrices,
- Density Peaks (DP) algorithm

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References(15)

References

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