%0 Journal Article
%T SA-DBSCAN:A self-adaptive density-based clustering algorithm<br>SA-DBSCAN:一种自适应基于密度聚类算法
%A XIA Lu-Ning
%A JING Ji-Wu
%A <br>夏鲁宁
%A 荆继武
%J 中国科学院研究生院学报
%D 2009
%I 
%X 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.
%K DBSCAN
%K SA-DBSCAN<br>数据挖掘
%K 聚类
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=B5EDD921F3D863E289B22F36E70174A7007B5F5E43D63598017D41BB67247657&cid=B47B31F6349F979B&jid=67CDFDECD959936E166E0F72DE972847&aid=1B9477D9A29FD5577A46762A4BD8CDBE&yid=DE12191FBD62783C&vid=96C778EE049EE47D&iid=E158A972A605785F&sid=BB98BB04E861B6F5&eid=E49BED2EA9A8956B&journal_id=1002-1175&journal_name=中国科学院研究生院学报&referenced_num=1&reference_num=15