Possibilistic Clustering Using Non-Euclidean Distance

摘要：

This paper presents a novel fuzzy clustering algorithm called novel possibilistic c-means (NPCM) clustering algorithm. Possibilistic c-means model (PCM) has been proposed by Krishnapuram and Keller to resist noises. It is claimed that NPCM is the extension of PCM by introducing a non-Euclidean distance into PCM to replace the Euclidean distance used in PCM. Based on robust statistical point of view and influence function, the non-Euclidean distance is more robust than the Euclidean distance. So the NPCM algorithm is more robust than PCM. Moreover, with the new distance NPCM can deal with noises or outliers better than PCM and fuzzy c-means (FCM). The experimental results show the better performance of NPCM.

关键词： Fuzzy Clustering Possibilistic C-Means Non-Euclidean Distance

作者: Bin Wu Lei Wang Cunliang Xu

作者单位: School of Software, Dalian University of Technology, Dalian 116620, China

会议类型: 国际会议

会议名称: 2009年中国控制与决策会议(2009 Chinese Control and Decision Conference)

会议地点: 广西桂林

会议语种:英文

页码: 938-940

在线出版日期: 2009-06-17（万方平台首次上网日期，不代表论文的发表时间）