Fuzzy Association Rule Mining with Type-2 Membership Functions

被引：16
|
作者：
Chen, Chun-Hao [2 ]
Hong, Tzung-Pei [1 ,3 ]
Li, Yu [1 ]
机构：
[1] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung, Taiwan
[2] Tamkang Univ, Dept Comp Sci & Informat Engn, Taipei 251, Taiwan
[3] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan
来源：
INTELLIGENT INFORMATION AND DATABASE SYSTEMS, PT II | 2015年 / 9012卷
关键词：
Data mining; Fuzzy association rule; Membership functions; Type-2 fuzzy set; MULTIPLE MINIMUM SUPPORTS; SEQUENTIAL PATTERNS;
D O I：
10.1007/978-3-319-15705-4_13
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 fuzzy association rule mining approach with type-2 membership functions is proposed for dealing with data uncertainty. It first transfers quantitative values in transactions into type-2 fuzzy values. Then, according to a predefined split number of points, they are reduced to type-1 fuzzy values. At last, the fuzzy association rules are derived by using these fuzzy values. Experiments on a simulated dataset were made to show the effectiveness of the proposed approach.
引用
收藏
页码：128 / 134
页数：7
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