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Discovering Partial Periodic High Utility Itemsets in Temporal Databases

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Database and Expert Systems Applications (DEXA 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11707))

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Abstract

High Utility Itemset Mining (HUIM) is an important model with many real-world applications. Given a (non-binary) transactional database and an external utility database, the aim of HUIM is to discover all itemsets within the data that satisfy the user-specified minimum utility (minUtil) constraint. The popular adoption and successful industrial application of HUIM has been hindered by the following two limitations: (i) HUIM does not allow external utilities of items to vary over time and (ii) HUIM algorithms are inadequate to find recurring customer purchase behavior. This paper introduces a flexible model of Partial Periodic High Utility Itemset Mining (PPHUIM) to address these two problems. The goal of PPHUIM is to discover only those interesting high utility itemsets that are occurring at regular intervals in a given temporal database. An efficient depth-first search algorithm, called PPHUI-Miner (Partial Periodic High Utility Itemset-Miner), has been proposed to enumerate all partial periodic high-utility itemsets in temporal databases. Experimental results show that the proposed algorithm is efficient.

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Author information

Authors and Affiliations

  1. International Institute of Information Technology, Hyderabad, Hyderabad, India

    T. Yashwanth Reddy & P. Krishna Reddy

  2. The University of Tokyo, Tokyo, Japan

    R. Uday Kiran, Masashi Toyoda & Masaru Kitsuregawa

  3. National Institute of Information and Communications Technology, Tokyo, Japan

    R. Uday Kiran

  4. National Institute of Informatics, Tokyo, Japan

    Masaru Kitsuregawa

Authors
  1. T. Yashwanth Reddy
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  2. R. Uday Kiran
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  3. Masashi Toyoda
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  4. P. Krishna Reddy
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  5. Masaru Kitsuregawa
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Corresponding author

Correspondence to R. Uday Kiran .

Editor information

Editors and Affiliations

  1. Clausthal University of Technology, Clausthal-Zellerfeld, Germany

    Sven Hartmann

  2. Johannes Kepler University of Linz, Linz, Austria

    Josef Küng

  3. The University of Texas at Arlington, Arlington, TX, USA

    Sharma Chakravarthy

  4. Johannes Kepler University of Linz, Linz, Austria

    Gabriele Anderst-Kotsis

  5. Software Competence Center Hagenberg, Hagenberg im Mühlkreis, Austria

    A Min Tjoa

  6. Johannes Kepler University of Linz, Linz, Austria

    Ismail Khalil

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Yashwanth Reddy, T., Kiran, R.U., Toyoda, M., Krishna Reddy, P., Kitsuregawa, M. (2019). Discovering Partial Periodic High Utility Itemsets in Temporal Databases. In: Hartmann, S., Küng, J., Chakravarthy, S., Anderst-Kotsis, G., Tjoa, A., Khalil, I. (eds) Database and Expert Systems Applications. DEXA 2019. Lecture Notes in Computer Science(), vol 11707. Springer, Cham. https://doi.org/10.1007/978-3-030-27618-8_26

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  • DOI: https://doi.org/10.1007/978-3-030-27618-8_26

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-27617-1

  • Online ISBN: 978-3-030-27618-8

  • eBook Packages: Computer ScienceComputer Science (R0)

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