QuantMiner for mining quantitative association rules. 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.
Keywords for this software
References in zbMATH (referenced in 5 articles , 1 standard article )
Showing results 1 to 5 of 5.
- Lai, Junzuo; Li, Yingjiu; Deng, Robert H.; Weng, Jian; Guan, Chaowen; Yan, Qiang: Towards semantically secure outsourcing of association rule mining on categorical data (2014)
- Minaei-Bidgoli, B.; Barmaki, R.; Nasiri, M.: Mining numerical association rules via multi-objective genetic algorithms (2013)
- Salleb-Aouissi, Ansaf; Vrain, Christel; Nortet, Cyril; Kong, Xiangrong; Rathod, Vivek; Cassard, Daniel: QuantMiner for mining quantitative association rules (2013)
- Alatas, Bilal; Akin, Erhan: Rough particle swarm optimization and its applications in data mining (2008)
- Ke, Yiping; Cheng, James; Ng, Wilfred: An information-theoretic approach to quantitative association rule mining (2008)