The Improvement of Initial Point Selection Method for Fuzzy K-Prototype Clustering Algorithm

摘要：

K-Prototype is one of the important and effective clustering analysis algorithm to deal with mixed data types. This article discussed fuzzy clustering algorithm based on K-Prototype in detail and made improvements to solve its initial value problems. The proposed method is simple, easy to understand and can be achieved easily.

关键词： K-Prototype clustering analysis mixed data types initial value

作者: Zhou Caiying Huang Longjun

作者单位: Science& Technology Division ,JiangXi University of Science and Technology ,GanZhou, China Software of Software ,JiangXi Normal University ,NanChang,China

会议类型: 国际会议

会议名称: 2010 2nd International Conference on Education Technology and Computer(第二届IEEE教育技术与计算机国际会议 ICETC 2010)

会议地点: 上海

会议语种:英文

页码: 549-552

在线出版日期: 2010-06-22（万方平台首次上网日期，不代表论文的发表时间）