Clustering Algorithm Based on Characteristics of Density Distribution

摘要：

Density-based clustering algorithms, which are important algorithms for the task of class identification in spatial database, have many advantages such as no dependence on the number of clusters, ability to discover clusters with arbitrary shapes and handle noise. However, clustering quality of most density-based clustering algorithms degrades when the clusters are of different densities. To address this issue, this paper brings forward a clustering algorithm based on characteristics of density distribution—CCDD algorithm. Firstly, it divides data space into a number of grids. Secondly, it re-divides data space into many smaller partitions, according to each grids one-dimensional or multi-dimensional characteristics of density distribution. Finally, it uses an improved DBSCAN algorithm, which chooses different parameters according to each partitions local density, to cluster respectively. The experimental results show that CCDD algorithm, which is superior in quality and efficiency to DBSCAN algorithm, can find clusters with arbitrary shapes and different densities in spatial databases with noise.

关键词： data mining clustering density-based clustering DBSCAN algorithm grid data space partition

作者: Zheng Hua Wang Zhenxing Zhang Liancheng Wang Qian

作者单位: National Digital Switching System Engineering & Technological R&D Center Zhengzhou, China

会议类型: 国际会议

会议名称: The 2nd IEEE International Conference on Advanced Computer Control(第二届先进计算机控制国际会议 ICACC 2010)

会议地点: 沈阳

会议语种:英文

页码: 431-435

在线出版日期: 2010-03-27（万方平台首次上网日期，不代表论文的发表时间）