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Inductive Logic Programming: Techniques and Applications
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High Performance OLAP and Data Mining on Parallel Computers
Data Mining and Knowledge Discovery
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Information Sciences: an International Journal - Special issue: Soft computing data mining
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GECCO '05 Proceedings of the 7th annual conference on Genetic and evolutionary computation
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Artificial Intelligence Review
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AIAP'07 Proceedings of the 25th conference on Proceedings of the 25th IASTED International Multi-Conference: artificial intelligence and applications
Fitness Function Comparison for GA-Based Feature Construction
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Dynamic Aggregation of Relational Attributes Based on Feature Construction
ADBIS '08 Proceedings of the 12th East European conference on Advances in Databases and Information Systems
Ant Colony Optimization for Genome-Wide Genetic Analysis
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Feature Construction and Feature Selection in Presence of Attribute Interactions
HAIS '09 Proceedings of the 4th International Conference on Hybrid Artificial Intelligence Systems
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Applied Soft Computing
Error detection and impact-sensitive instance ranking in noisy datasets
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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Artificial Intelligence in Medicine
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Applied Soft Computing
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This is a review paper, whose goal is tosignificantly improve our understanding of thecrucial role of attribute interaction in datamining. The main contributions of this paperare as follows. Firstly, we show that theconcept of attribute interaction has a crucialrole across different kinds of problem in datamining, such as attribute construction, copingwith small disjuncts, induction of first-orderlogic rules, detection of Simpson's paradox,and finding several types of interesting rules.Hence, a better understanding of attributeinteraction can lead to a better understandingof the relationship between these kinds ofproblems, which are usually studied separatelyfrom each other. Secondly, we draw attention tothe fact that most rule induction algorithmsare based on a greedy search which does notcope well with the problem of attributeinteraction, and point out some alternativekinds of rule discovery methods which tend tocope better with this problem. Thirdly, wediscussed several algorithms and methods fordiscovering interesting knowledge that,implicitly or explicitly, are based on theconcept of attribute interaction.