False positive item set algorithm for incremental association rule discovery

Amornchewin, Ratchadaporn; Kreesuradej, Worapoj

False positive item set algorithm for incremental association rule discovery

Author(s)

Amornchewin, Ratchadaporn

Kreesuradej, Worapoj

Date Issued

December 1, 2009

Type

Article

Abstract

In a dynamic database where the new transaction are inserted into the database, keeping patterns up-to-date and discovering new pattern are challenging problems of great practical importance. This may introduce new association rules and some existing association rules would become invalid. Thus, the maintenance of association rules for dynamic database is an important problem. In this paper, false positive itemset algorithm, which is an incremental algorithm, is proposed to deal with this problem. The proposed algorithm uses maximum support count of 1-itemsets obtained from previous mining to estimate infrequent itemsets, called false positive itemsets, of an original database. False positive itemsets will capable of being frequent itemsets when new transactions are inserted into an original database. This can reduce a number of times to scan an original database. This paper also proposes a new updating and pruning algorithm that guarantee to find all frequent itemsets of an updated database efficiently. The simulation results show that the proposed algorithm has a good performance.

Citation

International Journal of Multimedia and Ubiquitous Engineering, 4(2), 1-12, 2009