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Journal Articles Journal of Machine Learning Research Year : 2013

QuantMiner for Mining Quantitative Association Rules

QuantMiner for Mining Quantitative Association Rules

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.
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Dates and versions

hal-00912453 , version 1 (07-01-2021)

Identifiers

  • HAL Id : hal-00912453 , version 1

Cite

Ansaf Salleb-Aouissi, Christel Vrain, Cyril Nortet, Xiangrong Kong, Vivek Rathod, et al.. QuantMiner for Mining Quantitative Association Rules. Journal of Machine Learning Research, 2013, 14, pp.3153-3157. ⟨hal-00912453⟩
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