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Wednesday, April 6 • 2:00pm - 4:30pm
QuantMiner for Mining Quantitative Association Rules [D12]

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We present QuantMiner, 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 exploits a recent and innovative research in using genetic algorithms for mining quantitative rules published in IJCAI 2007.   The system is based on a genetic algorithm that dynamically discovers “good” intervals in association rules by optimizing both the support and the confidence of the rules. The experiments on real and artificial databases have shown the usefulness of QuantMiner as an interactive, exploratory data-mining tool.    The software was published in the Journal of Machine Learning Research open source software.  http://jmlr.org/papers/v14/salleb-aouissi13a.html 

Demo/Poster Presenter
avatar for Ansaf Salleb-Aouissi

Ansaf Salleb-Aouissi

Lecturer in the Discipline of Computer Science, Columbia Engineering
Ansaf Salleb-Aouissi joined the Department of Computer Science as a Lecturer in Discipline in July 2015. Ansaf received her PhD in Computer Science from University of Orleans, France in 2003, after which she pursued her training as a postdoctoral fellow at INRIA, Rennes (France). She... Read More →
avatar for Antonio Moretti

Antonio Moretti

PhD Candidate in Computer Science, Columbia Engineering

Wednesday April 6, 2016 2:00pm - 4:30pm EDT
Roone Arledge Auditorium Lerner Hall, Columbia University 2920 Broadway, New York, NY 10040

Attendees (3)