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

共 50 条

[21] Multi-level fuzzy transforms image compression
Di Martino, Ferdinando
Sessa, Salvatore
[J]. JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING, 2019, 10 (07) : 2745 - 2756
[22] Fuzzy approach for multi-level programming problems
Shih, HS
Lai, YJ
Lee, ES
[J]. COMPUTERS & OPERATIONS RESEARCH, 1996, 23 (01) : 73 - 91
[23] Multi-level database mining using AFOPT data structure and adaptive support constrains
Pater, Mirela
Popescu, Daniela E.
[J]. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, 2008, 3 : 437 - 441
[24] Fast mining algorithm for multi-level association rule data under temporal constraints
Yicheng, Mu
[J]. International Journal of Information and Communication Technology, 2019, 15 (04): : 431 - 444
[25] Improved multi-level association rule in mining algorithm based on a multidimensional data cube
Wang, Yingjie
Yu, Lili
Wen, Qinrun
Liu, Congli
[J]. 2013 3RD INTERNATIONAL CONFERENCE ON CONSUMER ELECTRONICS, COMMUNICATIONS AND NETWORKS (CECNET), 2013, : 355 - 358
[26] The benefits of using prefix tree data structure in multi-level frequent pattern mining
Pater, Mirela
Popescu, Daniela E.
[J]. SOFA 2007: 2ND IEEE INTERNATIONAL WORKSHOP ON SOFT COMPUTING APPLICATIONS, PROCEEDINGS, 2007, : 179 - +
[27] Hierarchical Bayesian learning framework for multi-level modeling using multi-level data
Jia, Xinyu
Papadimitriou, Costas
[J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2022, 179
[28] Multi-Level Interpolation for Inference with Sparse Fuzzy Rules: An Extended Way of Generating Multi-Level Points
Uehara, Kiyohiko
Hirota, Kaoru
[J]. JOURNAL OF ADVANCED COMPUTATIONAL INTELLIGENCE AND INTELLIGENT INFORMATICS, 2013, 17 (02) : 127 - 148
[29] Multi-level data fusion method
Lan, JH
Ma, BH
Zhou, ZY
[J]. ISTM/2001: 4TH INTERNATIONAL SYMPOSIUM ON TEST AND MEASUREMENT, VOLS 1 AND 2, CONFERENCE PROCEEDINGS, 2001, : 235 - 238
[30] NOTE ON CLASSIFICATION OF MULTI-LEVEL DATA
LANCE, GN
WILLIAMS, WT
[J]. COMPUTER JOURNAL, 1967, 9 (04): : 381 - &

← 1 2 3 4 5 →