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EFP-M2: Efficient Model for Mining Frequent Patterns in Transactional Database

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Computational Collective Intelligence. Technologies and Applications (ICCCI 2012)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7654))

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Abstract

Discovering frequent patterns plays an essential role in many data mining applications. The aim of frequent patterns is to obtain the information about the most common patterns that appeared together. However, designing an efficient model to mine these patterns is still demanding due to the capacity of current database size. Therefore, we propose an Efficient Frequent Pattern Mining Model (EFP-M2) to mine the frequent patterns in timely manner. The result shows that the algorithm in EFP-M2l is outperformed at least at 2 orders of magnitudes against the benchmarked FP-Growth.

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Author information

Authors and Affiliations

  1. Faculty of Computer System and Software Engineering, Universiti Malaysia Pahang, Malaysia

    Tutut Herawan & A. Noraziah

  2. Department of Computer Science, Universiti Malaysia Terengganu, Malaysia

    Zailani Abdullah

  3. Faculty of Science Computer and Information Technology, Universiti Tun Hussein Onn Malaysia, Malaysia

    Mustafa Mat Deris

  4. Scholl of Information Technology, Deakin University, Australia

    Jemal H. Abawajy

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

Editors and Affiliations

  1. Institute of Informatics, Wroclaw University of Technology, Wyb. Wyspianskiego 27, 50-370, Wroclaw, Poland

    Ngoc-Thanh Nguyen

  2. University of Information Technology, National Vietnam University VNU-HCM, Ho Chi Minh city, Vietnam

    Kiem Hoang

  3. Gdynia Maritime University, Str. Morska 81-87,, 81-225, Gdynia, Poland

    Piotr Jȩdrzejowicz

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© 2012 Springer-Verlag Berlin Heidelberg

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Herawan, T., Noraziah, A., Abdullah, Z., Deris, M.M., Abawajy, J.H. (2012). EFP-M2: Efficient Model for Mining Frequent Patterns in Transactional Database. In: Nguyen, NT., Hoang, K., Jȩdrzejowicz, P. (eds) Computational Collective Intelligence. Technologies and Applications. ICCCI 2012. Lecture Notes in Computer Science(), vol 7654. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-34707-8_4

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  • DOI: https://doi.org/10.1007/978-3-642-34707-8_4

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-34706-1

  • Online ISBN: 978-3-642-34707-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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