基于偶对约束和马氏距离的半监督模糊聚类算法

A semi-supervised fuzzy clustering algorithm based on pairwise constraints and Mahalanobis distance

研究了基于偶对约束的半监督模糊聚类,将马氏距离引入到半监督模糊聚类SCAPC(semi-supervised fuzzy clustering algorithm with pairwise constraints)中,获得了一种新的半监督模糊聚类目标函数,通过求解优化问题,提出了一种基于偶对约束和马氏距离的半监督模糊聚类算法M-SCAPC(ModifiedSCAPC).针对选择的标准数据集和人工数据集,对提出的算法M-SCAPC进行了实验研究,并与FCM(fuzzy C-means)、AFCC(active fuzzy constrained clustering)和SCAPC算法的聚类性能进行了比较,表明了提出的算法M-SCAPC在收敛速度和正确率方面的有效性.

The semi-supervised fuzzy clustering based on pair-wise constraints which introduces Mahalanobis distance into SCAPC（Semi-supervised fuzzy Clustering Algorithm with Pairwise Constraints）algorithm is mainly studied.And a new semi-supervised fuzzy clustering objective function is obtained.By solving the optimization problem,a semi-supervised fuzzy clustering algorithm M-SCAPC （Modified SCAPC） based on pairwise constraints and Mahalanobis distance is proposed.And some experimental researches are conducted for M-SCAPC algorithm using the selected standard data set and the artificial data set.Besides,clustering performance on M-SCAPC algorithm are compared with that of FCM（Fuzzy C-Means）,AFCC （Active Fuzzy Constrained Clustering） and SCAPC algorithms.From the results,M-SCAPC is effective in the convergence speed and the accuracy.