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Memory Efficient Frequent Itemset Mining

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Machine Learning and Data Mining in Pattern Recognition (MLDM 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10935))

Abstract

Frequent itemset mining has been one of the most popular data mining techniques. Despite a large number of algorithms developed to implement this functionality, there is still room for improvement of their efficiency. In this paper, we focus on memory use in frequent itemset mining. We propose a new approach in which transactions are represented in a compact graph with the number of nodes equal to the number of distinct items in a database. Our experimental results confirm the efficiency of memory use without significantly sacrificing the execution time of the mining algorithm.

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Notes

  1. 1.

    Edge labeling will be defined later.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, York University, Toronto, Canada

    Nima Shahbazi, Rohollah Soltani & Jarek Gryz

Authors
  1. Nima Shahbazi
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  2. Rohollah Soltani
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  3. Jarek Gryz
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Corresponding authors

Correspondence to Nima Shahbazi , Rohollah Soltani or Jarek Gryz .

Editor information

Editors and Affiliations

  1. Institute of Computer Vision and Applied Computer Sciences, Leipzig, Germany

    Petra Perner

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Shahbazi, N., Soltani, R., Gryz, J. (2018). Memory Efficient Frequent Itemset Mining. In: Perner, P. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2018. Lecture Notes in Computer Science(), vol 10935. Springer, Cham. https://doi.org/10.1007/978-3-319-96133-0_2

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  • DOI: https://doi.org/10.1007/978-3-319-96133-0_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-96132-3

  • Online ISBN: 978-3-319-96133-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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