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 quantitative association rules in large relational tables
SIGMOD '96 Proceedings of the 1996 ACM SIGMOD international conference on Management of data
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Integrating membership functions and fuzzy rule sets from multiple knowledge sources
Fuzzy Sets and Systems
An evolutionary algorithm to discover numeric association rules
Proceedings of the 2002 ACM symposium on Applied computing
Multi-Objective Optimization Using Evolutionary Algorithms
Multi-Objective Optimization Using Evolutionary Algorithms
Evolutionary Algorithms for Solving Multi-Objective Problems
Evolutionary Algorithms for Solving Multi-Objective Problems
Beyond Market Baskets: Generalizing Association Rules to Dependence Rules
Data Mining and Knowledge Discovery
Discretization: An Enabling Technique
Data Mining and Knowledge Discovery
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and 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
The Pareto Envelope-Based Selection Algorithm for Multi-objective Optimisation
PPSN VI Proceedings of the 6th International Conference on Parallel Problem Solving from Nature
On the Discovery of Interesting Patterns in Association Rules
VLDB '98 Proceedings of the 24rd International Conference on Very Large Data Bases
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
A Micro-Genetic Algorithm for Multiobjective Optimization
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
The Niched Pareto Genetic Algorithm 2 Applied to the Design of Groundwater Remediation Systems
EMO '01 Proceedings of the First International Conference on Evolutionary Multi-Criterion Optimization
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
Approximating the Nondominated Front Using the Pareto Archived Evolution Strategy
Evolutionary Computation
Interestingness measures for data mining: A survey
ACM Computing Surveys (CSUR)
Computers and Operations Research
A Hellinger-based discretization method for numeric attributes in classification learning
Knowledge-Based Systems
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
A discretization algorithm based on Class-Attribute Contingency Coefficient
Information Sciences: an International Journal
Muiltiobjective optimization using nondominated sorting in genetic algorithms
Evolutionary Computation
KEEL: a software tool to assess evolutionary algorithms for data mining problems
Soft Computing - A Fusion of Foundations, Methodologies and Applications - Special Issue on Evolutionary and Metaheuristics based Data Mining (EMBDM); Guest Editors: José A. Gámez, María J. del Jesús, José M. Puerta
Expert Systems with Applications: An International Journal
Handbook of Parametric and Nonparametric Statistical Procedures
Handbook of Parametric and Nonparametric Statistical Procedures
Soft Computing - A Fusion of Foundations, Methodologies and Applications
Multiobjective optimization problems with complicated Pareto sets, MOEA/D and NSGA-II
IEEE Transactions on Evolutionary Computation
Strength pareto particle swarm optimization and hybrid ea-pso for multi-objective optimization
Evolutionary Computation
Analysis of the Effectiveness of the Genetic Algorithms based on Extraction of Association Rules
Fundamenta Informaticae - Intelligent Data Analysis in Granular Computing
HCS: a new local search strategy for memetic multiobjective evolutionary algorithms
IEEE Transactions on Evolutionary Computation
Expert Systems with Applications: An International Journal
Evolutionary multi objective optimization for rule mining: a review
Artificial Intelligence Review
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
A fast and elitist multiobjective genetic algorithm: NSGA-II
IEEE Transactions on Evolutionary Computation
Performance assessment of multiobjective optimizers: an analysis and review
IEEE Transactions on Evolutionary Computation
MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition
IEEE Transactions on Evolutionary Computation
Genetic-Fuzzy Data Mining With Divide-and-Conquer Strategy
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Fuzzy Systems
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Some researchers have framed the extraction of association rules as a multi-objective problem, jointly optimizing several measures to obtain a set with more interesting and accurate rules. In this paper, we propose a new multi-objective evolutionary model which maximizes the comprehensibility, interestingness and performance of the objectives in order to mine a set of quantitative association rules with a good trade-off between interpretability and accuracy. To accomplish this, the model extends the well-known Multi-objective Evolutionary Algorithm Non-dominated Sorting Genetic Algorithm II to perform an evolutionary learning of the intervals of the attributes and a condition selection for each rule. Moreover, this proposal introduces an external population and a restarting process to the evolutionary model in order to store all the nondominated rules found and improve the diversity of the rule set obtained. The results obtained over real-world datasets demonstrate the effectiveness of the proposed approach.