A Density-based Clustering Algorithm For Uncertain Data

摘要：

As the development of the data acquisition technology, the research of the uncertain data has been the center of peoples attention, and at the same time, the cluster of the uncertain data has been in use widely. In this paper, after studying the cluster of the uncertain data, considering characters of the uncertain data, we propose a improved algorithm. We called it En-DBSCAN. In order to adapt the request of uncertain datas clustering we add probability factors and the theory of information entropy. The algorithm brings in a conception of probability radius to adjust uncertain datas scope of EPS neighborhood and information entropy to reduce center points indeterminacy. Besides, this paper gives an analysis and confirmation about the algorithm.

关键词： uncertain density-based En-DBSCAN probability distance information entropy

作者: Hongmei Wang Yingying Wang Shitao Wan

作者单位: College of Computer Science and Technology,JiLin University,Changchun,China College of Computer Scie College of Computer Science and Engineering,Changchun University of Technology,Changchun, China

会议类型: 国际会议

会议名称: 2012 International Conference on Computer Science and Electronic Engineering(2012 IEEE计算机科学与电子工程国际会议 ICCSEE 2012)

会议地点: 杭州

会议语种:英文

页码: 102-105

在线出版日期: 2012-03-23（万方平台首次上网日期，不代表论文的发表时间）