MOGA for Multi-Level Fuzzy Data Mining

被引：0

作者：

Chen, Chun-Hao ^{[1
]}

Ho, Chi-Hsuan ^{[1
]}

Hong, Tzung-Pei ^{[2
]}

Lin, Wei-Tee ^{[3
]}

机构：

[1] Tamkang Univ, Dept Comp Sci & Infonnat Engn, Taipei, Taiwan

[2] Natl Univ Kaohsiung, Dept Comp Sci & Informat Engn, Kaohsiung, Taiwan

[3] Natl Cheng Kung Univ, Dept Comp Sci & Informat Engn, Tainan, Taiwan

来源：

2012 IEEE INTERNATIONAL CONFERENCE ON GRANULAR COMPUTING (GRC 2012) | 2012年

关键词：

data mining; multi-objective genetic algorithm; multi-concept levels; membership function; fuzzy association rule; LEVEL ASSOCIATION RULES; MEMBERSHIP FUNCTIONS; GENETIC ALGORITHMS;

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：

In this paper, we propose a Multi-Objective Multi-Level Genetic-Fuzzy Mining (MOMLGFM) algorithm for mining a set of non-dominated membership functions for mining multi-level fuzzy association rules. It first encodes the membership functions of each item class (category) into a chromosome according to the given taxonomy. The two objective functions of each chromosome are then calculated. The first one is the summation of large 1-itemsets of each item in different concept levels, and the second one is the suitability of membership functions. The fitness value of each individual is then evaluated by these two objective functions. After the GA process terminates, various sets of membership functions could be used for deriving multiple-level fuzzy association rules according to decision maker. Experimental results on a simulation dataset also show the effectiveness of the algorithm.

引用

页码：32 / 37

页数：6

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