LI Kun-Lun1, CAO Zheng1, CAO Li-Ping2, ZHANG Chao1, LIU Ming1
1.College of Electronic and Information Engineering, Hebei University, Baoding 071002 2.Department of Electrical and Mechanical Engineering, Baoding Vocational and Technical College, Baoding 071051
Abstract Small amount of labeled data are used in semi-supervised clustering algorithms to improve the performance of the algorithms. It is a research hotspot in pattern recognition and its related fields. In this paper, some developments on semi-supervised clustering are introduced including constraint-based, distance-based and the combination of them. Using semi-supervised strategy to fuzzy C-means, a semi-supervised fuzzy C-means (constrained FCM) algorithm is proposed. Experimental results show that the proposed method obtains better accuracy compared with FCM and semi-supervised K-means.
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2009, Vol. 22 
