Volume 5, Issue 1
Development of a Weighted Fuzzy C-Means Clustering Algorithm based on Jade

Kangshun Li, Chuhu Zhang, Zhangxin Chen & Yan Ch

DOI:

Int. J. Numer. Anal. Mod. B, 5 (2014), pp. 113-122

Published online: 2014-05

Preview Full PDF 371 1193
Export citation
  • Abstract

To overcome the shortcomings of falling into local optimal solutions and being too sensitive to initial values of the traditional fuzzy C-mean clustering algorithm, a weighted fuzzy C-means (FCM) clustering algorithm based on adaptive differential evolution (JADE) is proposed in this paper. To consider the particular contributions of different features, a ReliefF algorithm is used to assign the weight for each feature. A weighted morphology-similarity distance (WMSD) based on ReliefF instead of the Euclidean distance is used to improve the objective function of the FCM clustering algorithm. Experimental results on the international standard Iris data and the contrast experimental results with other evolution algorithms show that the proposed algorithm has higher clustering accuracy and greater searching capability.

  • Keywords

  • AMS Subject Headings

  • Copyright

COPYRIGHT: © Global Science Press

  • Email address
  • BibTex
  • RIS
  • TXT
@Article{IJNAMB-5-113, author = {Kangshun Li, Chuhu Zhang, Zhangxin Chen and Yan Ch}, title = {Development of a Weighted Fuzzy C-Means Clustering Algorithm based on Jade}, journal = {International Journal of Numerical Analysis Modeling Series B}, year = {2014}, volume = {5}, number = {1}, pages = {113--122}, abstract = {To overcome the shortcomings of falling into local optimal solutions and being too sensitive to initial values of the traditional fuzzy C-mean clustering algorithm, a weighted fuzzy C-means (FCM) clustering algorithm based on adaptive differential evolution (JADE) is proposed in this paper. To consider the particular contributions of different features, a ReliefF algorithm is used to assign the weight for each feature. A weighted morphology-similarity distance (WMSD) based on ReliefF instead of the Euclidean distance is used to improve the objective function of the FCM clustering algorithm. Experimental results on the international standard Iris data and the contrast experimental results with other evolution algorithms show that the proposed algorithm has higher clustering accuracy and greater searching capability.}, issn = {}, doi = {https://doi.org/}, url = {http://global-sci.org/intro/article_detail/ijnamb/223.html} }
TY - JOUR T1 - Development of a Weighted Fuzzy C-Means Clustering Algorithm based on Jade AU - Kangshun Li, Chuhu Zhang, Zhangxin Chen & Yan Ch JO - International Journal of Numerical Analysis Modeling Series B VL - 1 SP - 113 EP - 122 PY - 2014 DA - 2014/05 SN - 5 DO - http://dor.org/ UR - https://global-sci.org/intro/article_detail/ijnamb/223.html KW - AB - To overcome the shortcomings of falling into local optimal solutions and being too sensitive to initial values of the traditional fuzzy C-mean clustering algorithm, a weighted fuzzy C-means (FCM) clustering algorithm based on adaptive differential evolution (JADE) is proposed in this paper. To consider the particular contributions of different features, a ReliefF algorithm is used to assign the weight for each feature. A weighted morphology-similarity distance (WMSD) based on ReliefF instead of the Euclidean distance is used to improve the objective function of the FCM clustering algorithm. Experimental results on the international standard Iris data and the contrast experimental results with other evolution algorithms show that the proposed algorithm has higher clustering accuracy and greater searching capability.
Kangshun Li, Chuhu Zhang, Zhangxin Chen & Yan Ch. (1970). Development of a Weighted Fuzzy C-Means Clustering Algorithm based on Jade. International Journal of Numerical Analysis Modeling Series B. 5 (1). 113-122. doi:
Copy to clipboard
The citation has been copied to your clipboard