A new feature weighted fuzzy clustering algorithm

Authors:
Jie Li;Xinbo Gao;Licheng Jiao
Affiliations:
School of Electronic Engineering, Xidian Univ., Xi'an, P.R. China;School of Electronic Engineering, Xidian Univ., Xi'an, P.R. China;School of Electronic Engineering, Xidian Univ., Xi'an, P.R. China
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Year:
2005

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Abstract

In the field of cluster analysis, the fuzzy k-means, k-modes and k-prototypes algorithms were designed for numerical, categorical and mixed data sets respectively. However, all the above algorithms assume that each feature of the samples plays an uniform contribution for cluster analysis. To consider the particular contributions of different features, a novel feature weighted fuzzy clustering algorithm is proposed in this paper, in which the ReliefF algorithm is used to assign the weights for every feature. By weighting the features of samples, the above three clustering algorithms can be unified, and better classification results can be also achieved. The experimental results with various real data sets illustrate the effectiveness of the proposed algorithm.