A Semi-supervised Clustering Algorithm Based on Rough Reduction

摘要：

Clustering analysis is an important issue in data mining fields. Clustering in high dimensional space is especially difficult for a series of problems, such as the sparseness of spatial distribution of data, too much noise data points. Based on the analysis of current clustering algorithms can not get satisfying clustering results of high dimensional data. The theory of rough set and the idea of semi-supervised are introduced. And a semi-supervised grid clustering algorithm RSGrid based on the reduction of rough set theory is proposed. The theoretical analysis and experimental results indicate the algorithm can solve the problem of clustering in high dimensional space efficiently.

关键词： reduction clustering semi-supervised data mining

作者: Liandong Lin Wei Qu Xiang Yu

作者单位: Key Laboratory of Electronics Engineering, College of Heilongjiang Province Heilongjiang University Department of Computer Science and Technology University of Harbin Engineering Harbin, Heilongjiang

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: 5427-5431

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）