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International Conference on Discovery Science

DS 2017: Discovery Science pp 251–266Cite as

Mining Strongly Closed Itemsets from Data Streams

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10558))

Abstract

We consider the problem of mining strongly closed itemsets from transactional data streams. Compactness and stability against changes in the input are two characteristic features of this kind of itemsets that make them appealing for different applications. Utilizing their algebraic and algorithmic properties, we propose an algorithm based on reservoir sampling for approximating this type of itemsets in the landmark streaming setting, prove its correctness, and show empirically that it yields a considerable speed-up over a straightforward naive algorithm without any significant loss in precision and recall. As a motivating application, we experimentally demonstrate the suitability of strongly closed itemsets to concept drift detection in transactional data streams.

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Notes

  1. 1.

    Due to space limitations we omit frequency constraints in this short version.

  2. 2.

    We note that Hoeffding’s inequality applies to samples without replacement as well [5]. A tighter bound can be derived from Serfling’s inequality [12]. The improvement becomes however marginal with increasing data stream length.

  3. 3.

    http://fimi.ua.ac.be/data/.

  4. 4.

    We are going to present further practical applications (e.g., computer aided product configuration) in the long version of this paper.

References

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Author information

Authors and Affiliations

  1. Fraunhofer IAIS, Schloss Birlinghoven, 53754, St. Augustin, Germany

    Daniel Trabold & Tamás Horváth

  2. Department of Computer Science, University of Bonn, Bonn, Germany

    Tamás Horváth

Authors
  1. Daniel Trabold
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  2. Tamás Horváth
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Corresponding author

Correspondence to Daniel Trabold .

Editor information

Editors and Affiliations

  1. Kyoto University, Kyoto, Japan

    Akihiro Yamamoto

  2. Hokkaido University, Sapporo, Japan

    Takuya Kida

  3. National Institute of Informatics, Tokyo, Japan

    Takeaki Uno

  4. Gakushuin University, Tokyo, Japan

    Tetsuji Kuboyama

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Trabold, D., Horváth, T. (2017). Mining Strongly Closed Itemsets from Data Streams. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds) Discovery Science. DS 2017. Lecture Notes in Computer Science(), vol 10558. Springer, Cham. https://doi.org/10.1007/978-3-319-67786-6_18

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  • DOI: https://doi.org/10.1007/978-3-319-67786-6_18

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-67785-9

  • Online ISBN: 978-3-319-67786-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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