A Novel Semi-Supervised Fuzzy C-Means Clustering Method

摘要：

In this paper we propose a novel semi-supervised fuzzy c-means algorithm. We introduce a seed set which contains a small amount of labeled data. First, generating an initial partition in the seed set, we use the center of each partition as the cluster center and optimize the objective function of FCM using EM algorithm. Experiments results show that, our method can avoid the defect of fuzzy c-means that is sensitive to the initial centers partly and give much better partition accuracy.

关键词： Semi-supervised Fuzzy c-means EM

作者: Kunlun Li Zheng Cao Liping Cao Rui Zhao

作者单位: College of Electronic and Information Engineering, Hebei University, Baoding 071002,China Department of Electrical and Mechanical Engineering, Baoding Vocational and Technical College, Baodi

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: 3761-3765

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）