MOGAMOD: Multi-objective genetic algorithm for motif discovery
Expert Systems with Applications: An International Journal
Discovering Interesting Classification Rules with Particle Swarm Algorithm
Advanced Web and NetworkTechnologies, and Applications
Motif discovery using multi-objective genetic algorithm in biosequences
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
Product portfolio identification with data mining based on multi-objective GA
Journal of Intelligent Manufacturing
IEEE Transactions on Fuzzy Systems
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part I
International Journal of Applied Metaheuristic Computing
MOGA-based fuzzy data mining with taxonomy
Knowledge-Based Systems
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Association rules form one of the most widely used techniques to discover correlations among attribute in a database. So far, some efficient methods have been proposed to obtain these rules with respect to an optimal goal, such as: to maximize the number of large itemsets and interesting rules or the values of support and confidence for the discovered rules. This paper first introduces optimized fuzzy association rule mining in terms of three important criteria; strongness, interestingness and comprehensibility. Then, it proposes multi-objective Genetic Algorithm (GA) based approaches for discovering these optimized rules. Optimization technique according to given criterion may be one of two different forms; The first tries to determine the appropriate fuzzy sets of quantitative attributes in a prespecified rule, which is also called as certain rule. The second deals with finding both uncertain rules and their appropriate fuzzy sets. Experimental results conducted on a real data set show the effectiveness and applicability of the proposed approach.