Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining optimized association rules for numeric attributes
PODS '96 Proceedings of the fifteenth ACM SIGACT-SIGMOD-SIGART symposium on Principles of database systems
Data mining methods for knowledge discovery
Data mining methods for knowledge discovery
Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set
IEEE Transactions on Knowledge and Data Engineering
Analyzing the Subjective Interestingness of Association Rules
IEEE Intelligent Systems
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Discovering Numeric Association Rules via Evolutionary Algorithm
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Multi-objective rule mining using genetic algorithms
Information Sciences: an International Journal - Special issue: Soft computing data mining
Evolutionary Multiobjective Optimization: Theoretical Advances and Applications (Advanced Information and Knowledge Processing)
Mining Fuzzy Weighted Association Rules
HICSS '07 Proceedings of the 40th Annual Hawaii International Conference on System Sciences
An information-theoretic approach to quantitative association rule mining
Knowledge and Information Systems
Rough particle swarm optimization and its applications in data mining
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
QuantMiner: a genetic algorithm for mining quantitative association rules
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Genetic algorithm based framework for mining fuzzy association rules
Fuzzy Sets and Systems
Information Sciences: an International Journal
Toward boosting distributed association rule mining by data de-clustering
Information Sciences: an International Journal
Expert Systems with Applications: An International Journal
A multi-objective evolutionary approach for subgroup discovery
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Cluster-Based Evaluation in Fuzzy-Genetic Data Mining
IEEE Transactions on Fuzzy Systems
Reliability assessment and failure analysis of lithium iron phosphate batteries
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
Software defect prediction using relational association rule mining
Information Sciences: an International Journal
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Association rule discovery is an ever increasing area of interest in data mining. Finding rules for attributes with numerical values is still a challenging point in the process of association rule discovery. Most of popular methods for association rule mining cannot be applied to the numerical data without data discretization. There have been efforts to resolve the problem of dealing with numeric data. These approaches suffer from problems which are discussed in this paper. This work proposes a multi-objective genetic algorithm approach for mining association rules for numerical data. Several measures are defined in order to determine more efficient rules. Three measures, confidence, interestingness, and comprehensibility have been used as different objectives for our multi objective optimization which is amplified with genetic algorithms approach. Finally, the best rules are obtained through Pareto optimality. This method is based on the notion of rough patterns that use rough values defined with upper and lower intervals to represent a range or set of values. Mutation and crossover operators give a powerful exploration ability to the method and allow it to find out the best intervals of existing numerical values. The experimental results show that the generated rules by this method are more appropriate - based on several different characteristics - than the similar approaches' results, and our method outperforms these methods.