Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Pincer Search: A New Algorithm for Discovering the Maximum Frequent Set
EDBT '98 Proceedings of the 6th International Conference on Extending Database Technology: Advances in Database Technology
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Multi-objective rule mining using genetic algorithms
Information Sciences: an International Journal - Special issue: Soft computing data mining
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Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special issue (1143 - 1198) " Distributed Bioinspired Algorithms"; Guest editors: F. Fernández de Vega, E. Cantú-Paz
Expert Systems with Applications: An International Journal
Genetic algorithm based framework for mining fuzzy association rules
Fuzzy Sets and Systems
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Classification based on association rules: A lattice-based approach
Expert Systems with Applications: An International Journal
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MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
Unsupervised linkage learner based on local optimums
MCPR'12 Proceedings of the 4th Mexican conference on Pattern Recognition
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ICCSA'12 Proceedings of the 12th international conference on Computational Science and Its Applications - Volume Part III
CAR-Miner: An efficient algorithm for mining class-association rules
Expert Systems with Applications: An International Journal
A clustering ensemble based on a modified normalized mutual information metric
AMT'12 Proceedings of the 8th international conference on Active Media Technology
Mining numerical association rules via multi-objective genetic algorithms
Information Sciences: an International Journal
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Journal of Computer and System Sciences
QAR-CIP-NSGA-II: A new multi-objective evolutionary algorithm to mine quantitative association rules
Information Sciences: an International Journal
Multi-objective PSO algorithm for mining numerical association rules without a priori discretization
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Multi objective processing can be leveraged for mining the association rules. This paper discusses the application of multi objective genetic algorithm to association rule mining. We focus our attention especially on association rule mining. This paper proposes a method based on genetic algorithm without taking the minimum support and confidence into account. In order to improve algorithm efficiency, we apply the FP-tree algorithm. Our method extracts the best rules that have best correlation between support and confidence. The operators of our method are flexible for changing the fitness. Unlike the Apriori-based algorithm, it does not depend on support. Experimental study shows that our technique outperforms the traditional methods.