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Bounding Box Representation of Co-location Instances for \(L_{\infty }\) Induced Distance Measure

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Big Data Analytics and Knowledge Discovery (DaWaK 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12925))

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Abstract

In this paper, we investigate the efficiency of Co-location Pattern Mining (CPM). In popular methods for CPM, the most time-consuming step consists of identifying of pattern instances, which are required to calculate the potential interestingness of the pattern. We tackle this problem and provide an instance identification method that has lower complexity than the state-of-the-art approach: (1) we introduce a new representation of co-location instances based on bounding boxes, (2) we formulate and prove several theorems regarding such a representation that can improve instances identification step, (3) we provide a novel algorithm utilizing the aforementioned theorems and analyze its complexity. Finally, we experimentally demonstrate the efficiency of the proposed solution.

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Notes

  1. 1.

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Acknowledgement

This research has been partially supported by the statutory funds of Poznan University of Technology.

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Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, Piotrowo 2, 60-965, Poznan, Poland

    Witold Andrzejewski & Pawel Boinski

Authors
  1. Witold Andrzejewski
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  2. Pawel Boinski
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Corresponding authors

Correspondence to Witold Andrzejewski or Pawel Boinski .

Editor information

Editors and Affiliations

  1. University of Bologna, Bologna, Forli/Cesena, Italy

    Matteo Golfarelli

  2. Poznań University of Technology, Poznan, Poland

    Robert Wrembel

  3. Johannes Kepler University Linz, Linz, Austria

    Gabriele Kotsis

  4. TU Wien, Vienna, Austria

    A Min Tjoa

  5. Johannes Kepler University Linz, Linz, Austria

    Ismail Khalil

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Andrzejewski, W., Boinski, P. (2021). Bounding Box Representation of Co-location Instances for \(L_{\infty }\) Induced Distance Measure. In: Golfarelli, M., Wrembel, R., Kotsis, G., Tjoa, A.M., Khalil, I. (eds) Big Data Analytics and Knowledge Discovery. DaWaK 2021. Lecture Notes in Computer Science(), vol 12925. Springer, Cham. https://doi.org/10.1007/978-3-030-86534-4_1

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  • DOI: https://doi.org/10.1007/978-3-030-86534-4_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-86533-7

  • Online ISBN: 978-3-030-86534-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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