Abstract
Semi-supervised clustering is a method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering which introduces Mahalanobis distance and Gaussian Kernel. And we obtain a new semi-supervised fuzzy clustering objective function. By solving the optimization problem, we propose a semi-supervised fuzzy clustering algorithm F-SCAPC which includes F(M)-SCAPC and F(K)-SCAPC. And we do experimental research for proposed algorithm F-SCAPC using the selected standard data set and the artificial data set. Besides, we compare performance of presented algorithm F-SCAPC with AFFC, KFCM-F and SCAPC algorithms. From the results, we can see that F-SCAPC is effective in the convergence speed and the accuracy.
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