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

页码：1985 / 1989

页数：5

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