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Parallel FP-Growth on PC Cluster

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Advances in Knowledge Discovery and Data Mining (PAKDD 2003)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 2637))

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Abstract

FP-growth has become a popular algorithm to mine frequent patterns. Its metadata FP-tree has allowed significant performance improvement over previously reported algorithms. However that special data structure also restrict the ability for further extensions. There is also potential problem when FP-tree can not fit into the memory. In this paper, we report parallel execution of FP-growth. We examine the bottlenecks of the parallelization and also method to balance the execution efficiently on shared-nothing environment.

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References

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Author information

Authors and Affiliations

  1. Institute of Industrial Science, The University of Tokyo, 4-6-1 Komaba, Meguro-ku, Tokyo, 153-8505, Japan

    Iko Pramudiono & Masaru Kitsuregawa

Authors
  1. Iko Pramudiono
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  2. Masaru Kitsuregawa
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Editor information

Editors and Affiliations

  1. Computer Science Department, Korea Advanced Institute of Science and Technology, 373-1 Koo-Sung Dong, Yoo-Sung Ku, Daejeon, 305-701, Korea

    Kyu-Young Whang

  2. Department of Statistics, Seoul National University, Sillimdong Kwanakgu, Seoul, 151-742, Korea

    Jongwoo Jeon

  3. School of Electrical Engineering and Computer Science, Seoul National University, Kwanak P.O. Box 34, Seoul, 151-742, Korea

    Kyuseok Shim

  4. Department of Computer Science and Engineering, University of Minnesota, 200 Union St SE, Minneapolis, MN, 55455, USA

    Jaideep Srivastava

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© 2003 Springer-Verlag Berlin Heidelberg

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Pramudiono, I., Kitsuregawa, M. (2003). Parallel FP-Growth on PC Cluster. In: Whang, KY., Jeon, J., Shim, K., Srivastava, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2003. Lecture Notes in Computer Science(), vol 2637. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36175-8_47

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  • DOI: https://doi.org/10.1007/3-540-36175-8_47

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-04760-5

  • Online ISBN: 978-3-540-36175-6

  • eBook Packages: Springer Book Archive

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