An Improved Semi-supervised Fuzzy Clustering Algorithm
نویسندگان
چکیده
Semi-supervised clustering is an important method which can improve clustering performance by introducing partial supervised information. This paper mainly studies the semi-supervised fuzzy clustering based on Mahalanobis distance and Gaussian Kernel for SCAPC algorithm. Here, we give a new semi-supervised fuzzy clustering objective function. By solving the optimization problem with above objective function, we obtain 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 one of FCM, CA, AFFC, KCA, KFCM-F and SCAPC algorithms. From the results, we can see that F-SCAPC is effective in the convergence speed and the clustering accuracy. Keywords-Semi-supervised clustering; Pairwise constraints; Mahalanobis distance; Gaussian Kernel
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