Rough Set Approach for Categorical Data Clustering

被引：0
|
作者：
Herawan, Tutut [1 ]
Yanto, Iwan Tri Riyadi [1 ]
Deris, Mustafa Mat [1 ]
机构：
[1] Univ Tun Hussein Onn Malaysia, FTMM, Johor Baharu, Malaysia
来源：
DATABASE THEORY AND APPLICATION | 2009年 / 64卷
关键词：
Clustering; Categorical data; Rough set theory; Attributes dependencies; ALGORITHM;
D O I：
暂无
中图分类号：
TP3 [计算技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, we focus our discussion on the rough set approach for categorical data clustering. We propose MADE (Maximal Attributes Dependency), an alternative technique for categorical data clustering using rough set theory taking into account maximal attributes dependencies. Experimental results on two benchmark UCI datasets show that MADE technique is better with the baseline categorical data clustering techniques with respect to computational complexity and clusters purity.
引用
收藏
页码：179 / 186
页数：8
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