A Fuzzy Clustering Algorithm Based on Initial Optimization for Mixed Data

摘要：

In this paper, we propose an improved fuzzy C-means clustering algorithm. It aims at solving the problems which need a large quantity of calculation in traditional fuzzy C-means (FCM) clustering algorithm. The improved algorithm can determine k value and the initial cluster centers according to the results of incremental clustering and can reduce the number of iterations; at the same time it can handle the data set with mixed attributes.

关键词： fuzzy clustering initial optimization mixed data

作者: Yang Lan Yan Xiong Junsheng Guo

作者单位: School of Computer and Information Technology, Xinyang Normal University, Xinyang Henan 464000 China School of Economics and Management, Xinyang Normal University, Xinyang Henan 464000 China

会议类型: 国际会议

会议名称: 2010 International Conference on Information Technology and Industrial Engineering(2010年信息技术与工业工程国际学术会议 ITIE 2010)

会议地点: 武汉

会议语种:英文

页码: 1042-1045

在线出版日期: 2010-06-06（万方平台首次上网日期，不代表论文的发表时间）