A Human-Computer Cooperation Fuzzy c-Means Clustering with Interval-Valued Weights

In this paper, a fuzzy c‐means clustering algorithm based on interval‐valued weights is proposed for improving clustering performance. In the proposed algorithm, the interval‐valued weights are first constructed by synergy of the ReliefF algorithm and the analytic hierarchy process (AHP) method, and...

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Veröffentlicht in:International journal of intelligent systems Jg. 30; H. 2; S. 81 - 98
Hauptverfasser: Lu, Wei, Zhang, Liyong, Liu, Xiaodong, Yang, Jianhua, Pedrycz, Witold
Format: Journal Article
Sprache:Englisch
Veröffentlicht: New York Blackwell Publishing Ltd 01.02.2015
John Wiley & Sons, Inc
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ISSN:0884-8173, 1098-111X
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Zusammenfassung:In this paper, a fuzzy c‐means clustering algorithm based on interval‐valued weights is proposed for improving clustering performance. In the proposed algorithm, the interval‐valued weights are first constructed by synergy of the ReliefF algorithm and the analytic hierarchy process (AHP) method, and then they are transformed into a constraint condition associating with each weight variable in the weighted clustering objective function. In the sequence, the weighted clustering objective function is solved by combining the Lagrange multiplier method with the gradient‐based iteration computation. In the whole process of algorithm iteration, a compulsion strategy with human–computer cooperation is adopted to ensure each weight variable satisfies interval constraint itself. Three well‐known data set are used to perform profound experiments. Experimental results clearly show that the proposed algorithm has better clustering performance than other the weighted fuzzy c‐means clustering algorithm.
Bibliographie:ArticleID:INT21683
istex:DA5678F51265CE63F9407015D4C1514947E34F10
Natural Science Foundation of China - No. 61175041
ark:/67375/WNG-JD2W88CJ-3
Canada Research Chair (Witold Pedrycz)
Major National Science and Technology Programs of China in the "Twelfth-Five Year" Plan Period - No. 2011ZX05039-3-3
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ISSN:0884-8173
1098-111X
DOI:10.1002/int.21683