An efficient algorithm for mining important association rule from multi-relational database using distributed mining ideas. Most existing data mining approaches look for rules in a single data table. However, most databases are multi-relational. In this paper, we present a novel distributed data-mining method to mine important rules in multiple tables (relations) and combine the method with genetic algorithm to enhance the mining efficiency. Genetic algorithm is in charge of finding antecedent rules and aggregate of transaction set that produces the corresponding rule from the chief attributes. Apriori and statistic method is in charge of mining consequent rules from the rest relational attributes of other tables according to the corresponding transaction set producing the antecedent rule in a distributed way. Our method has several advantages over most exiting data mining approaches. First, it can process multi-relational database efficiently. Second, rules produced have finer pattern. Finally, we adopt a new concept of extended association rules that contain more import and underlying information.
['Wenxiang Dou', 'Jinglu Hu', 'Kotaro Hirasawa', 'Gengfeng Wu']
Distributed multi-relational data mining based on genetic algorithm