ROBUST EXTENSION OF FCM ALGORITHM

摘要：

Clustering is a procedure through which objects are distinguished or classified in accordance with their similarity.The fuzzy c-means method (FCM) is one of the most popular clustering methods based on minimization of a criterion function. However, the FCM method is sensitive to the presence of noise and outliers in data. A new clustering algorithm is proposed by extending the criterion function,which includes the well-known fuzzy c-means method as its special case. Numerical experiments show that the new clustering algorithm is less sensitive than the traditional FCM method and robust to outliers.

关键词： Fuzzy clustering fuzzy c-means criterion function

作者: CHENG-JIA LI

作者单位: School of Science, Hangzhou Dianzi University, Hangzhou, 310018, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 1388-1393

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）