Clustering categorical data using coverage density

被引：0
|
作者：
Yan, H [1 ]
Zhang, L [1 ]
Zhang, Y [1 ]
机构：
[1] Univ Elect Sci & Technol China, Sch Engn & Comp Sci, Computat Intelligence Lab, Chengdu 610054, Peoples R China
来源：
ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS | 2005年 / 3584卷
关键词：
D O I：
暂无
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
In this paper, a new algorithm based on the idea of coverage density is proposed for clustering categorical data. It uses average coverage density as the global criterion function. Large sparse categorical databases can be clusitered effectively by using this algorithm. It shows that the algorithm uses less memory and time by analyzing its time and space complexity. Experiments on two real datasets are carried out to illustrate the performance of the proposed algorithm.
引用
收藏
页码：248 / 255
页数：8
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