MINING QUANTITATIVE ASSOCIATION RULES WITH STANDARD SQL QUERIES AND IT’S EVALUATION

Association rule is an important model in data mining. Currently, more research work is done on the algorithm of mining boolean association rules than on the algorithm of quantitative association rules. With standard SQL, which has the advantages of powerful function, easy development, quick running speed, and strong reliability etc , to implement the quantitative association rules mining can result in good performance. Firstly, quantitative attribute needs to be dispersed with language field theory. Then association rules are mined by improving Apriori algorithm with SQL. Additionly, the association rules are evaluated with the sufficiency gene LS of subjectivity Bayes reasoning. This algorithm has been applied to the Lujiang Insect Pests Database, and the experiment result shows that this algorithm is quick, effective, easy to develop, and can be widely applied.