MSDBSCAN: Multi-density Scale-Independent Clustering Algorithm Based on DBSCAN

摘要：

A good approach in data mining is density based clustering in which the clusters are constructed based on the density of shape regions. The prominent algorithm proposed in density based clustering family is DBSCAN 1 that uses two global density parameters, namely minimum number of points for a dense region and epsilon indicating the neighborhood distance. Among others, one of the weaknesses of this algorithm is its un-suitability for multi-density data sets where different regions have various densities so the same epsilon does not work. In this paper, a new density based clustering algorithm, MSDBSCAN, is proposed. MSDBSCAN uses a new definition for core point and dense region. The MSDBSCAN can find clusters in multi-variant density data sets. Also this algorithm benefits scale independency. The results obtained on data sets show that the MSDBSCAN is very effective in multi-variant environment.

关键词： local core distance scale independency multi-density scale-independent clustering MSDBSCAN

作者: Gholamreza Esfandani Hassan Abolhassani

作者单位: Computer Engineering Department Sharif University of Technology Tehran Iran

会议类型: 国际会议

会议名称: 6th International Conference on Advanced Data Mining and Applications(第六届先进数据挖掘及应用国际会议 ADMA 2010)

会议地点: 重庆

会议语种:英文

页码: 202-213

在线出版日期: 2010-11-19（万方平台首次上网日期，不代表论文的发表时间）