A novel semi-supervised fuzzy C-means clustering method

Authors:
Kunlun Li;Zheng Cao;Liping Cao;Rui Zhao
Affiliations:
College of Electronic and Information Engineering, Hebei University, Baoding, China;College of Electronic and Information Engineering, Hebei University, Baoding, China;Department of Electrical and Mechanical Engineering, Baoding Vocational and Technical College, Baoding, China;College of Electronic and Information Engineering, Hebei University, Baoding, China
Venue:
CCDC'09 Proceedings of the 21st annual international conference on Chinese control and decision conference
Year:
2009

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Abstract

In this paper we propose a novel semi-supervised fuzzy c-means algorithm. We introduce a seed set which contains a small amount of labeled data. First, generating an initial partition in the seed set, we use the center of each partition as the cluster center and optimize the objective function of FCM using EM algorithm. Experiments results show that, our method can avoid the defect of fuzzy c-means that is sensitive to the initial centers partly and give much better partition accuracy.