Authors: Sixue Bai; Shilin Duan
Addresses: School of Information Engineering, Nanchang University, No. 999 University Avenue, Nanchang, Jiangxi Province, 330031, China ' School of Information Engineering, Nanchang University, No. 999 University Avenue, Nanchang, Jiangxi Province, 330031, China
Abstract: Excavating potential multidimensional valuable association rules from big data has wide application. The main association rule mining algorithm Apriori has the bottlenecks of scanning repeatedly database and generating big number of candidate sets, though the FP algorithm does not generate candidate sets, but FP-tree cannot handle the problem of storage and traversal of big data. In addition, Apriori and FP-growth algorithm needs to reconstruct association rules while implementing increment mining, its not available for growth-oriented data mining. Facing those problems, designing DB-growth algorithm based on relational database table SourceIndex, applying string combinate to generate pattern, insert or update database to construct frequent sets, mining association rules by querying database. In addition, it supports increment mining and depth mining.
Keywords: association rules mining; apriori algorithm; FP-growth algorithm; DB-growth algorithm; increment mining; depth mining; relational databases; big data; data mining.
International Journal of Granular Computing, Rough Sets and Intelligent Systems, 2015 Vol.4 No.1, pp.1 - 12
Available online: 16 Feb 2016 *Full-text access for editors Access for subscribers Purchase this article Comment on this article