Improved Fuzzy Clustering Method Based on Entropy Coefficient and Its Application

  • Authors:

  • Li Liu;Jianzhong Zhou;Xueli An;Yinghai Li;Qiang Liu

  • Affiliations:

  • College of Hydroelectric and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan, China 430074;College of Hydroelectric and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan, China 430074;College of Hydroelectric and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan, China 430074;College of Hydroelectric and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan, China 430074;College of Hydroelectric and Digitalization Engineering, Huazhong University of Science and Technology, Wuhan, China 430074

  • Venue:
  • ISNN '08 Proceedings of the 5th international symposium on Neural Networks: Advances in Neural Networks, Part II
  • Year:
  • 2008

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Abstract

Based on the principle of fuzzy clustering analysis and the theory of entropy, an improved fuzzy clustering method is given by improving the method of establishing the membership function, combining the clustering weight with the entropy coefficient, and replacing the Zadeh operator M($\bigvee,\bigwedge$) with the weight average operator M(±, 茂戮驴). With the improved method, the zeroweight problem is addressed effectively, the weights of each factor are modified properly and the phenomenon of Major Factor Dominating is also alleviated appropriately. Finally, an illustrative example is given to clarify the method, which shows that the improved fuzzy clustering method is reasonable, feasible, simple and practical.