QuantMiner for mining quantitative association rules

Authors:
Ansaf Salleb-Aouissi;Christel Vrain;Cyril Nortet;Xiangrong Kong;Vivek Rathod;Daniel Cassard
Affiliations:
Center for Computational Learning Systems, Columbia University, New York, NY;Laboratoire d'Informatique Fondamentale d'Orléans, Université d'Orléans, Orléans Cedex 2, France;Laboratoire d'Informatique Fondamentale d'Orléans, Université d'Orléans, Orléans Cedex 2, France;Center for Computational Learning Systems, Columbia University, New York, NY;Center for Computational Learning Systems, Columbia University, New York, NY;French Geological Survey, Orléans Cedex 2, France
Venue:
The Journal of Machine Learning Research
Year:
2013

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Abstract

In this paper, we propose QUANTMINER, a mining quantitative association rules system. This system is based on a genetic algorithm that dynamically discovers "good" intervals in association rules by optimizing both the support and the confidence. The experiments on real and artificial databases have shown the usefulness of QUANTMINER as an interactive, exploratory data mining tool.