Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Data Mining and Knowledge Discovery with Evolutionary Algorithms
Set-Oriented Mining for Association Rules in Relational Databases
ICDE '95 Proceedings of the Eleventh International Conference on Data Engineering
Multiple Objective Optimization with Vector Evaluated Genetic Algorithms
Proceedings of the 1st International Conference on Genetic Algorithms
Genetic Algorithms for Multiobjective Optimization: FormulationDiscussion and Generalization
Proceedings of the 5th International Conference on Genetic Algorithms
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
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Expert Systems with Applications: An International Journal
MOPSO: a proposal for multiple objective particle swarm optimization
CEC '02 Proceedings of the Evolutionary Computation on 2002. CEC '02. Proceedings of the 2002 Congress - Volume 02
Application of elitist multi-objective genetic algorithm for classification rule generation
Applied Soft Computing
Computers & Mathematics with Applications
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
TACO-miner: An ant colony based algorithm for rule extraction from trained neural networks
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
Evolutionary approach for mining association rules on dynamic databases
PAKDD'03 Proceedings of the 7th Pacific-Asia conference on Advances in knowledge discovery and data mining
Application of particle swarm optimization to association rule mining
Applied Soft Computing
Expert Systems with Applications: An International Journal
A novel evolutionary method to search interesting association rules by keywords
Expert Systems with Applications: An International Journal
Mining data streams with concept drifts using genetic algorithm
Artificial Intelligence Review
Expert Systems with Applications: An International Journal
A multi-objective genetic algorithm approach to rule mining for affective product design
Expert Systems with Applications: An International Journal
Mining of multiobjective non-redundant association rules in data streams
ICAISC'12 Proceedings of the 11th international conference on Artificial Intelligence and Soft Computing - Volume Part II
Mining numerical association rules via multi-objective genetic algorithms
Information Sciences: an International Journal
Hi-index | 12.05 |
In the domain of association rules mining (ARM) discovering the rules for numerical attributes is still a challenging issue. Most of the popular approaches for numerical ARM require a priori data discretization to handle the numerical attributes. Moreover, in the process of discovering relations among data, often more than one objective (quality measure) is required, and in most cases, such objectives include conflicting measures. In such a situation, it is recommended to obtain the optimal trade-off between objectives. This paper deals with the numerical ARM problem using a multi-objective perspective by proposing a multi-objective particle swarm optimization algorithm (i.e., MOPAR) for numerical ARM that discovers numerical association rules (ARs) in only one single step. To identify more efficient ARs, several objectives are defined in the proposed multi-objective optimization approach, including confidence, comprehensibility, and interestingness. Finally, by using the Pareto optimality the best ARs are extracted. To deal with numerical attributes, we use rough values containing lower and upper bounds to show the intervals of attributes. In the experimental section of the paper, we analyze the effect of operators used in this study, compare our method to the most popular evolutionary-based proposals for ARM and present an analysis of the mined ARs. The results show that MOPAR extracts reliable (with confidence values close to 95%), comprehensible, and interesting numerical ARs when attaining the optimal trade-off between confidence, comprehensibility and interestingness.