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DFP-Growth: An Efficient Algorithm for Mining Frequent Patterns in Dynamic Database

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Information Computing and Applications (ICICA 2012)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 7473))

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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.

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Author information

Authors and Affiliations

  1. Department of Computer Science, University Malaysia Terengganu, 21030, Kuala Terengganu, Terengganu, Malaysia

    Zailani Abdullah

  2. Faculty of Computer Systems and Software Engineering, University Malaysia Pahang Lebuhraya Tun Razak, 26300, Kuantan, Pahang, Malaysia

    Tutut Herawan & A. Noraziah

  3. Faculty of Computer Science and Information Technology, University Tun Hussein Onn Malaysia Parit Raja, Batu Pahat, 86400, Johor, Malaysia

    Mustafa Mat Deris

Authors
  1. Zailani Abdullah
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  2. Tutut Herawan
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  3. A. Noraziah
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  4. Mustafa Mat Deris
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Editor information

Editors and Affiliations

  1. College of Science, Hebei United University, 063000, Tangshan, Hebei, China

    Baoxiang Liu

  2. Nanyang Technological University, Singapore

    Maode Ma

  3. College of Science, Hebei United University, 063009, Tangshan, Hebei, China

    Jincai Chang

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© 2012 Springer-Verlag Berlin Heidelberg

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Abdullah, Z., Herawan, T., Noraziah, A., Deris, M.M. (2012). DFP-Growth: An Efficient Algorithm for Mining Frequent Patterns in Dynamic Database. In: Liu, B., Ma, M., Chang, J. (eds) Information Computing and Applications. ICICA 2012. Lecture Notes in Computer Science, vol 7473. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34062-8_7

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  • DOI: https://doi.org/10.1007/978-3-642-34062-8_7

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34061-1

  • Online ISBN: 978-3-642-34062-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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