Genetic-Fuzzy Mining with Type-2 Membership Functions

被引：0
|
作者：
Li, Yu [1 ]
Chen, Chun-Hao [2 ]
Hong, Tzung-Pei [1 ,3 ]
Lee, Yeong-Chyi [4 ]
机构：
[1] Natl Sun Yat Sen Univ, Dept Comp Sci & Engn, Kaohsiung 80424, Taiwan
[2] Tamkang Univ, Dept Comp Sci & Informat Engn, Taipei, Taiwan
[3] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan
[4] Cheng Shiu Univ, Dept Informat Management, Kaohsiung, Taiwan
来源：
2014 IEEE INTERNATIONAL CONFERENCE ON FUZZY SYSTEMS (FUZZ-IEEE) | 2014年
关键词：
ASSOCIATION RULES; CLASSIFICATION; ALGORITHMS; SYSTEM; LOGIC; MODEL; SETS;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
In this paper, a type-2 genetic-fuzzy mining algorithm is proposed for mining a set of type-2 membership functions for mining fuzzy association rules. It first encodes the type-2 membership functions of each item into a chromosome. The quantitative transactions are then transformed into fuzzy values according to the type-2 membership functions. Each chromosome is then evaluated by the number of large 1-itemsets and the suitability factor. The suitability factor consists of three sub-factors - coverage, overlap and difference which are used to avoid three bad types of membership functions. Experiments on a simulated dataset are also conducted to show the effectiveness of the proposed approach.
引用
收藏
页码：1985 / 1989
页数：5
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