Improving fuzzy c-means clustering based on feature-weight learning

Authors:
Xizhao Wang;Yadong Wang;Lijuan Wang
Affiliations:
Department of Mathematics and Computer Science, Hebei University, Baoding, Hebei 071002, China;Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongjiang 150001, China;Department of Mathematics and Computer Science, Hebei University, Baoding, Hebei 071002, China and Department of Computer Science and Engineering, Harbin Institute of Technology, Harbin, Heilongji ...
Venue:
Pattern Recognition Letters
Year:
2004

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Abstract

Feature-weight assignment can be regarded as a generalization of feature selection. That is, if all values of featureweights are either 1 or 0, feature-weight assignment degenerates to the special case of feature selection. Generally speaking, a number in [0,1] can be assigned to a feature for indicating the importance of the feature. This paper shows that an appropriate assignment of feature-weight can improve the performance of fuzzy c-means clustering. The weight assignment is given by learning according to the gradient descent technique. Experiments on some UCI databases demonstrate the improvement of performance of fuzzy c-means clustering.