Association analysis with one scan of databases

Abstract

Mining frequent patterns with an FP-tree avoids costly candidate generation and repeatedly occurrence frequency checking against the support threshold. It therefore achieves better performance and efficiency than Apriori-like algorithms. However the database still needs to be scanned twice to get the FP-tree. This can be very time-consuming when new data are added to an existing database because two scans may be needed for not only the new data but also the existing data. This paper presents a new data structure P-tree, Pattern Tree, and a new technique, which can get the P-tree through only one scan of the database and can obtain the corresponding FP-tree with a specified support threshold. Updating a P-tree with new data needs one scan of the new data only, and the existing data do not need to be re-scanned.