An efficient java implementation of a GA-based miner for relational association rules with numerical attributes
Hirohisa Seki, Masahiro Nagao · 2017
QuantMiner, proposed by Salleb-Aouissi et al., is one of the well-known systems for mining quantitative association rules using a genetic algorithm (GA). We have applied the GA-based methods of QuantMiner to multi-relational data mining (MRDM), where mining rules involves multiple relations from a relational database, and our preliminary experiments showed that a straightforward application of QuantMiner to MRDM has an efficiency problem. In this paper, we propose RelQM-J, a Java implementation of QuantMiner applied to multi-relational databases (MRDBs), with an aim to handle the computation of the supports of rules in an efficient way, by using a hash-based data structure. Our experimental results show the effectiveness of the proposed method, compared with the conventional method used in QuantMiner.