Semi-Supervised Possibilistic Fuzzy c-Means Clustering Algorithm on Maximized Central Distance

摘要：

　　Abandoning the constraint conditions of memberships in traditional fuzzy clustering algorithms,such as Fuzzy C-Mcans (FCM),Possibilistie Fuzzy c-Meaus (PCM) is more robust in dealing with noise and outliers.A small amount of labeled patterns guiding the clustering process are easy to be obtained in practical applications.In this study,a novel semi-supervised clustering technique titled semi-supervised possibilistic clustering (sPCM) is proposed.Because the PCM algorithm is easy to fall into identical clusters,we introduce the center maximization to overcome this difficulty.The proposed algorithm makes distance between different classes as far as possible,which can avoid identical clusters.The experimental results demonstrate that the accuracy of the proposed sPCM algorithm has been improved,making algorithm more robust by inheriting the characteristics of PCM.

关键词： PCM maximized central distance semi-supervised clustering robustness

作者: Li-Liu Xiao-Jun Wu

作者单位: School of IOT Engineering Jiangnan University Wuxi,China

会议类型: 国际会议

会议名称: 2013 2nd International Conference on Computer Science and Electronics Engineering(ICCSEE2013)(2013年第二届计算机科学与电子工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 1367-1371

在线出版日期: 2013-03-22（万方平台首次上网日期，不代表论文的发表时间）