Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
CURE: an efficient clustering algorithm for large databases
SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
A Statistical Theory for Quantitative Association Rules
Journal of Intelligent Information Systems
SIA: A Supervised Inductive Algorithm with Genetic Search for Learning Attributes based Concepts
ECML '93 Proceedings of the European Conference on Machine Learning
Discovering Numeric Association Rules via Evolutionary Algorithm
PAKDD '02 Proceedings of the 6th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Quantitative Association Rules Based on Half-Spaces: An Optimization Approach
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
A genetic feature weighting scheme for pattern recognition
Integrated Computer-Aided Engineering
First approach toward on-line evolution of association rules with learning classifier systems
Proceedings of the 10th annual conference companion on Genetic and evolutionary computation
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
Integrated Computer-Aided Engineering
An algorithm to mine general association rules from tabular data
Information Sciences: an International Journal
Genetic algorithm based framework for mining fuzzy association rules
Fuzzy Sets and Systems
Feature-based image registration by means of the CHC evolutionary algorithm
Image and Vision Computing
Mining quantitative association rules on overlapped intervals
ADMA'05 Proceedings of the First international conference on Advanced Data Mining and Applications
Natural Encoding for Evolutionary Supervised Learning
IEEE Transactions on Evolutionary Computation
Evolutionary Fuzzy Rule Induction Process for Subgroup Discovery: A Case Study in Marketing
IEEE Transactions on Fuzzy Systems
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
Discovering gene association networks by multi-objective evolutionary quantitative association rules
Journal of Computer and System Sciences
Optimising operational costs using Soft Computing techniques
Integrated Computer-Aided Engineering
Mining association rules with single and multi-objective grammar guided ant programming
Integrated Computer-Aided Engineering
Integrated Computer-Aided Engineering
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This research presents the mining of quantitative association rules based on evolutionary computation techniques. First, a real-coded genetic algorithm that extends the well-known binary-coded CHC algorithm has been projected to determine the intervals that define the rules without needing to discretize the attributes. The proposed algorithm is evaluated in synthetic datasets under different levels of noise in order to test its performance and the reported results are then compared to that of a multi-objective differential evolution algorithm, recently published. Furthermore, rules from real-world time series such as temperature, humidity, wind speed and direction of the wind, ozone, nitrogen monoxide and sulfur dioxide have been discovered with the objective of finding all existing relations between atmospheric pollution and climatological conditions.