Multi-objective genetic algorithms based automated clustering for fuzzy association rules mining

Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorith...

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Veröffentlicht in:Journal of intelligent information systems Jg. 31; H. 3; S. 243 - 264
Hauptverfasser: Alhajj, Reda, Kaya, Mehmet
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Boston Springer US 01.12.2008
Springer Nature B.V
Schlagworte:
Artificial Intelligence
Associations
Automation
Chromosomes
Clustering
Computer Science
Data mining
Data Structures and Information Theory
Fuzzy logic
Fuzzy sets
Genetic algorithms
Information Storage and Retrieval
IT in Business
Natural Language Processing (NLP)
Optimization
Set theory
Studies
Multi-objective genetic algorithms
CURE
Fuzziness
Automated clustering
Data mining
Fuzzy association rules
ISSN:0925-9902, 1573-7675
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Zusammenfassung:Researchers realized the importance of integrating fuzziness into association rules mining in databases with binary and quantitative attributes. However, most of the earlier algorithms proposed for fuzzy association rules mining either assume that fuzzy sets are given or employ a clustering algorithm, like CURE, to decide on fuzzy sets; for both cases the number of fuzzy sets is pre-specified. 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 achieve this by developing an automated clustering method based on multi-objective Genetic Algorithms (GA); the aim of the proposed approach is to automatically cluster values of a quantitative attribute in order to obtain large number of large itemsets in less time. We compare the proposed multi-objective GA based approach with two other approaches, namely: 1) CURE-based approach, which is known as one of the most efficient clustering algorithms; 2) Chien et al. clustering approach, which is an automatic interval partition method based on variation of density. Experimental results on 100 K transactions extracted from the adult data of USA census in year 2000 showed that the proposed automated clustering method exhibits good performance over both CURE-based approach and Chien et al.’s work in terms of runtime, number of large itemsets and number of association rules.
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ISSN:0925-9902
1573-7675
DOI:10.1007/s10844-007-0044-1