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
Data mining using two-dimensional optimized association rules: scheme, algorithms, and visualization
SIGMOD '96 Proceedings of the 1996 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
Association rules over interval data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
An updated survey of GA-based multiobjective optimization techniques
ACM Computing Surveys (CSUR)
Genetic Algorithms in Search, Optimization and Machine Learning
Genetic Algorithms in Search, Optimization and Machine Learning
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
ICDE '97 Proceedings of the Thirteenth International Conference on Data Engineering
Discovering Numeric Association Rules via Evolutionary Algorithm
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Population-Based Incremental Learning: A Method for Integrating Genetic Search Based Function Optimization and Competitive Learning
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
MIC Framework: An Information-Theoretic Approach to Quantitative Association Rule Mining
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Genetic algorithm based framework for mining fuzzy association rules
Fuzzy Sets and Systems
Multiobjective evolutionary algorithms: a comparative case studyand the strength Pareto approach
IEEE Transactions on Evolutionary Computation
Interactive search of rules in medical data using multiobjective evolutionary algorithms
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
Hybrid differential evolution based on fuzzy C-means clustering
Proceedings of the 11th Annual conference on Genetic and evolutionary computation
Multi-objective evolutionary algorithm based on adaptive discrete differential evolution
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
CEC'09 Proceedings of the Eleventh conference on Congress on Evolutionary Computation
A Point Symmetry-Based Automatic Clustering Approach Using Differential Evolution
ISICA '09 Proceedings of the 4th International Symposium on Advances in Computation and Intelligence
CIDE: Chaotically Initialized Differential Evolution
Expert Systems with Applications: An International Journal
Quantitative association rules applied to climatological time series forecasting
IDEAL'09 Proceedings of the 10th international conference on Intelligent data engineering and automated learning
Adaptive strategy selection in differential evolution
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Integrated Computer-Aided Engineering
A 2-Opt based differential evolution for global optimization
Applied Soft Computing
A clustering-based differential evolution for global optimization
Applied Soft Computing
Expert Systems with Applications: An International Journal
Hybrid differential evolution for global numerical optimization
RSKT'10 Proceedings of the 5th international conference on Rough set and knowledge technology
Photosynthetic algorithm approaches for bioinformatics
Expert Systems with Applications: An International Journal
Computational intelligence techniques for predicting earthquakes
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Analysis of measures of quantitative association rules
HAIS'11 Proceedings of the 6th international conference on Hybrid artificial intelligent systems - Volume Part II
Differential evolution for parameterized procedural woody plant models reconstruction
Applied Soft Computing
Expert Systems with Applications: An International Journal
Differential evolution with modified mutation strategy for solving global optimization problems
SEMCCO'11 Proceedings of the Second international conference on Swarm, Evolutionary, and Memetic Computing - Volume Part I
Expert Systems: The Journal of Knowledge Engineering
Mining numerical association rules via multi-objective genetic algorithms
Information Sciences: an International Journal
Accelerated biogeography-based optimization with neighborhood search for optimization
Applied Soft Computing
Association rule mining using binary particle swarm optimization
Engineering Applications of Artificial Intelligence
Mining frequent patterns and association rules using similarities
Expert Systems with Applications: An International Journal
Discovering gene association networks by multi-objective evolutionary quantitative association rules
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
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In this paper, a Pareto-based multi-objective differential evolution (DE) algorithm is proposed as a search strategy for mining accurate and comprehensible numeric association rules (ARs) which are optimal in the wider sense that no other rules are superior to them when all objectives are simultaneously considered. The proposed DE guided the search of ARs toward the global Pareto-optimal set while maintaining adequate population diversity to capture as many high-quality ARs as possible. ARs mining problem is formulated as a four-objective optimization problem. Support, confidence value and the comprehensibility of the rule are maximization objectives while the amplitude of the intervals which conforms the itemset and rule is minimization objective. It has been designed to simultaneously search for intervals of numeric attributes and the discovery of ARs which these intervals conform in only single run of DE. Contrary to the methods used as usual, ARs are directly mined without generating frequent itemsets. The proposed DE performs a database-independent approach which does not rely upon the minimum support and the minimum confidence thresholds which are hard to determine for each database. The efficiency of the proposed DE is validated upon synthetic and real databases.