收稿日期: 2008-06-26
修回日期: 2008-12-25
网络出版日期: 2009-07-15
基金资助
国家高技术研究发展计划(863)项目(2003AA144050)资助
SA-DBSCAN:A self-adaptive density-based clustering algorithm
Received date: 2008-06-26
Revised date: 2008-12-25
Online published: 2009-07-15
夏鲁宁 , 荆继武 . SA-DBSCAN:一种自适应基于密度聚类算法[J]. 中国科学院大学学报, 2009 , 26(4) : 530 -538 . DOI: 10.7523/j.issn.2095-6134.2009.4.015
DBSCAN is a classic density-based clustering algorithm. It can automatically determine the number of clusters and treat clusters of arbitrary shapes. In the clustering process of DBSCAN, two parameters, Eps and minPts,have to be specified by uses. In this paper an adaptive algorithm named SA-DBSCAN was introduced to determine the two parameters automatically via analysis of the statistical characteristics of the dataset, which enabled clustering process of DBSCAN fully automated. Experimental results indicate that SA-DBSCAN can select appropriate parameters and gain a rather high validity of clustering.
Key words： data mining; clustering; DBSCAN; SA-DBSCAN
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