DFP-Growth: an efficient algorithm for mining frequent patterns in dynamic database

Authors:
Zailani Abdullah;Tutut Herawan;A. Noraziah;Mustafa Mat Deris
Affiliations:
Department of Computer Science, University Malaysia Terengganu, Kuala Terengganu, Terengganu, Malaysia;Faculty of Computer Systems and Software Engineering, University Malaysia Pahang Lebuhraya Tun Razak, Kuantan, Pahang, Malaysia;Faculty of Computer Systems and Software Engineering, University Malaysia Pahang Lebuhraya Tun Razak, Kuantan, Pahang, Malaysia;Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia Parit Raja, Batu Pahat, Johor, Malaysia
Venue:
ICICA'12 Proceedings of the Third international conference on Information Computing and Applications
Year:
2012

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Abstract

Mining frequent patterns in a large database is still an important and relevant topic in data mining. Nowadays, FP-Growth is one of the famous and benchmarked algorithms to mine the frequent patterns from FP-Tree data structure. However, the major drawback in FP-Growth is, the FP-Tree must be rebuilt all over again once the original database is changed. Therefore, in this paper we introduce an efficient algorithm called Dynamic Frequent Pattern Growth (DFP-Growth) to mine the frequent patterns from dynamic database. Experiments with three UCI datasets show that the DFP-Growth is up to 1.4 times faster than benchmarked FP-Growth, thus verify it efficiencies.