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ClaSP: An Efficient Algorithm for Mining Frequent Closed Sequences

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Advances in Knowledge Discovery and Data Mining (PAKDD 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7818))

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Abstract

In this paper, we propose a new algorithm, called ClaSP for mining frequent closed sequential patterns in temporal transaction data. Our algorithm uses several efficient search space pruning methods together with a vertical database layout. Experiments on both synthetic and real datasets show that ClaSP outperforms currently well known state of the art methods, such as CloSpan.

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References

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Author information

Authors and Affiliations

  1. Information and Communication Engineering Dept., University of Murcia, Spain

    Antonio Gomariz & Roque Marin

  2. Languages and Systems Dept., University of Murcia, Spain

    Manuel Campos

  3. Mathematics and Computer Science Dept., University of Antwerp, Belgium

    Bart Goethals

Authors
  1. Antonio Gomariz
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  2. Manuel Campos
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  3. Roque Marin
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  4. Bart Goethals
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Editor information

Editors and Affiliations

  1. School of Computing Science, Simon Fraser University, 8888 University Drive, V5A 1S6, Burnaby, BC, Canada

    Jian Pei

  2. Dept. of Computer Science and Information Engineering, Institute of Medical Informatics, National Cheng Kung University, Tainan, Taiwan

    Vincent S. Tseng

  3. Faculty of Engineering and Information Technology, University of Technology Sydney, Broadway, P.O. Box 123, 2007, Sydney, NSW, Australia

    Longbing Cao  & Guandong Xu  & 

  4. Asian Office of Aerospace Research and Development (AOARD), Air Force Office of Scientific Research (AFOSR), Air Force Research Laboratory USA, Osaka University, 7-23-17 Roppongi, 106-0032, Minato-ku, Tokyo, Japan

    Hiroshi Motoda

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© 2013 Springer-Verlag Berlin Heidelberg

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Gomariz, A., Campos, M., Marin, R., Goethals, B. (2013). ClaSP: An Efficient Algorithm for Mining Frequent Closed Sequences. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2013. Lecture Notes in Computer Science(), vol 7818. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-37453-1_5

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  • DOI: https://doi.org/10.1007/978-3-642-37453-1_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-37452-4

  • Online ISBN: 978-3-642-37453-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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