MOGAMOD: Multi-objective genetic algorithm for motif discovery
Expert Systems with Applications: An International Journal
An ACS-based framework for fuzzy data mining
Expert Systems with Applications: An International Journal
Wrapping VRXQuery with self-adaptive fuzzy capabilities
Web Intelligence and Agent Systems
Automated extraction of extended structured motifs using multi-objective genetic algorithm
Expert Systems with Applications: An International Journal
Motif discovery using multi-objective genetic algorithm in biosequences
IDA'07 Proceedings of the 7th international conference on Intelligent data analysis
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In this paper, we propose an automated method to decide on the number of fuzzy sets and for the autonomous mining of both fuzzy sets and fuzzy association rules. We compare the proposed multi-objective GA based approach with: 1) CURE based approach; 2) Chien et al clustering approach. Experimental results on 100K transactions extracted from the adult data of United States census in year 2000 show that the proposed method exhibits good performance over the other two approaches in terms of runtime, number of large itemsets and number of association rules.