CCLS Researcher Ansaf Salleb-Aouissi, and co-workers' paper "QuantMiner for Mining Quantitative Association Rules" gets accepted for publication in the Journal of Machine Learning Research (JMLR) open source software

QuantMiner

CCLS Researcher Ansaf Salleb-Aouissi, and co-workers' paper "QuantMiner for Mining Quantitative Association Rules" gets accepted for publication  in the Journal of Machine Learning Research (JMLR) open source software http://jmlr.org/mloss/ . 

Authors: Ansaf Salleb-Aouissi, Christel Vrain , Cyril Nortet, Xiangrong Kong, Vivek Rathod, and Daniel Cassard.

The software will be published along with a paper. 

QuantMiner is a Data Mining tool for mining Quantitative Association Rules that is taking into consideration numerical attributes in the mining process without a binning /discretization a priori of the data. It uses a recent and innovative research in using genetic algorithms for mining quantitative rules published by the authors in IJCAI 2007. 

QuantMiner website: http://quantminer.github.com/QuantMiner/ 

Project repository for QuantMiner: https://github.com/QuantMiner/QuantMiner 

Paper: http://www1.ccls.columbia.edu/~ansaf/QuantMiner_paper.pdf