Extending OLAP with fuzziness for effective mining of fuzzy multidimensional weighted association rules

被引：0
|
作者：
Kaya, Mehmet [1 ]
Alhajj, Reda
机构：
[1] Firat Univ, Dept Comp Engn, Elazig, Turkey
[2] Univ Calgary, Dept Comp Sci, Calgary, AB T2N 1N4, Canada
[3] Global Univ, Dept Comp Sci, Beirut, Lebanon
来源：
ADVANCED DATA MINING AND APPLICATIONS, PROCEEDINGS | 2006年 / 4093卷
关键词：
association rules; data mining; fuzzy data cube; multidimensional mining; OLAP; weighted mining;
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：
This paper contributes to the ongoing research on multidimensional online association rules mining by proposing a general architecture that utilizes a fuzzy data cube combined with the concepts of weight and multiple-level to mine fuzzy weighted multi-cross-level association rules. We compared the proposed approach to an existing approach that does not utilize fuzziness. Experimental results on the adult data of the United States census in year 2000 demonstrate the effectiveness and applicability of the proposed fuzzy OLAP based mining approach.
引用
收藏
页码：64 / 71
页数：8
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