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. © 2012 Springer-Verlag.
CITATION STYLE
Abdullah, Z., Herawan, T., Noraziah, A., & Deris, M. M. (2012). DFP-growth: An efficient algorithm for mining frequent patterns in dynamic database. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 7473 LNCS, pp. 51–58). https://doi.org/10.1007/978-3-642-34062-8_7
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