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| Some Developments on Semi-Supervised Clustering |
| 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 |
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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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Received: 15 December 2008
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2009, Vol. 22 
