Anzeige
Erschienen in:
2012 | OriginalPaper | Buchkapitel
DFP-Growth: An Efficient Algorithm for Mining Frequent Patterns in Dynamic Database
verfasst von : Zailani Abdullah, Tutut Herawan, A. Noraziah, Mustafa Mat Deris
Erschienen in: Information Computing and Applications
Verlag: Springer Berlin Heidelberg
Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.
Wählen Sie Textabschnitte aus um mit Künstlicher Intelligenz passenden Patente zu finden. powered by
Markieren Sie Textabschnitte, um KI-gestützt weitere passende Inhalte zu finden. powered by
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.