MINING FUZZY ASSOCIATION RULES FROM DATABASE

被引：0

作者：

Tang, Hongxia ^{[1
]}

Pei, Zheng ^{[1
]}

Yi, Liangzhong ^{[1
]}

Zhang, Zunwei ^{[1
]}

机构：

[1] Xihua Univ, Sch Math & Comp Sci, Chengdu 610039, Peoples R China

来源：

INTELLIGENT DECISION MAKING SYSTEMS, VOL. 2 | 2010年

关键词：

Data Mining; Fuzzy Association Rules; Fuzzy Sets;

D O I：

10.1142/9789814295062_0038

中图分类号：

C93 [管理学]; O22 [运筹学];

学科分类号：

070105 ; 12 ; 1201 ; 1202 ; 120202 ;

摘要：

Association rules are used for describing association among attribute values in the field of data mining Inspired by the Apriori algorithm, the paper presents a new approach of mining fuzzy association rules There exists similarity between the new algorithm(NAL) and the Apriori algorithm, however, the NAL method can handle several data types (categories, list, number and linguistic term) at the same tune We adopt fuzzy techniques, so that all data types could be represented and operated from fuzzy points of view The novel method can be used to find many useful association rules

引用

页码：240 / +

页数：2