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2013 JMLR JMLR 2013

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. [abs] [ pdf ][ bib ] [ code ] © JMLR 2013. (edit, beta)

🌉 Interdisciplinary Bridge — Data Science & Analytics and Machine Learning
🧭 Keyword Pioneer — quantitative association rule
🐣 Hot Topic Early Bird — genetic algorithm
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Ansaf Salleb-Aouissi , Christel Vrain , Cyril Nortet , Xiangrong Kong , Vivek Rathod , Daniel Cassard

Topics

Machine Learning > Core Methods > Classification Machine Learning > Learning Types > Unsupervised Learning Machine Learning > Optimization & Theory > Optimization Data Science & Analytics > Methods > Data Mining

Keywords

data mining genetic algorithm association rule mining quantitative association rule quantitative rule
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