Mining Incremental Association Rules with Generalized FP-Tree
Conference of the Canadian Society for Computational Studies of Intelligence
New transaction insertions and old transaction deletions may lead to previously generated association rules no longer being interesting, and new interesting association rules may also appear.Existing association rules maintenance algorithms are Apriori-like, which mostly need to scan the entire database several times in order to update the previously computed frequent or large itemsets, and in particular, when some previous small itemsets become large in the updated database.This paper presents two new algorithms that use the frequent patterns tree (FP-tree) structure to reduce the required number of database scans. One proposed algorithm is the DB-tree algorithm, which stores all the database information in an FP-tree structure and requires no re-scan of the original database for all update cases. The second algorithm is the PotFp-tree (Potential frequent pattern) algorithm, which uses a prediction of future possible frequent itemsets to reduce the number of times the original database needs to be scanned when previous small itemsets become large after database update.
Ezeife, C. I. and Su, Y.. (2002). Mining Incremental Association Rules with Generalized FP-Tree. Conference of the Canadian Society for Computational Studies of Intelligence, 147-160.