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A Novel Sequential Pattern Mining Algorithm for Large Scale Data Sequences

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Computational Science and Its Applications – ICCSA 2022 Workshops (ICCSA 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13377))

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Abstract

Sequential pattern mining algorithms are unsupervised machine learning algorithms that allow finding sequential patterns on data sequences that have been put together based on a particular order. These algorithms are mostly optimized for finding sequential data sequences containing more than one element. Hence, we argue that there is a need for algorithms that are particularly optimized for data sequences that contain only one element. Within the scope of this research, we study the design and development of a novel algorithm that is optimized for data sets containing data sequences with single elements and that can detect sequential patterns with high performance. The time and memory requirements of the proposed algorithm are examined experimentally. The results show that the proposed algorithm has low running times, while it has the same accuracy results as the algorithms in the similar category in the literature. The obtained results are promising.

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Acknowledgements

This study was supported by BiletBank R &D Center. We would like to thank BiletBank for providing us with the necessary hardware and access to their datasets.

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Authors and Affiliations

  1. BiletBank Research and Development Center, Akdeniz PE-TUR A.S., Istanbul, Turkey

    Ali Burak Can & Meryem Uzun-Per

  2. Computer Engineering Department, Istanbul Health and Technology University, Istanbul, Turkey

    Meryem Uzun-Per

  3. Computer Engineering Department, Yildiz Technical University, Istanbul, Turkey

    Mehmet S. Aktas

Authors
  1. Ali Burak Can
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  2. Meryem Uzun-Per
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  3. Mehmet S. Aktas
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Corresponding author

Correspondence to Ali Burak Can .

Editor information

Editors and Affiliations

  1. University of Perugia, Perugia, Italy

    Osvaldo Gervasi

  2. University of Basilicata, Potenza, Potenza, Italy

    Beniamino Murgante

  3. Østfold University College, Halden, Norway

    Sanjay Misra

  4. University of Minho, Braga, Portugal

    Ana Maria A. C. Rocha

  5. University of Cagliari, Cagliari, Italy

    Chiara Garau

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Can, A.B., Uzun-Per, M., Aktas, M.S. (2022). A Novel Sequential Pattern Mining Algorithm for Large Scale Data Sequences. In: Gervasi, O., Murgante, B., Misra, S., Rocha, A.M.A.C., Garau, C. (eds) Computational Science and Its Applications – ICCSA 2022 Workshops. ICCSA 2022. Lecture Notes in Computer Science, vol 13377. Springer, Cham. https://doi.org/10.1007/978-3-031-10536-4_46

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  • DOI: https://doi.org/10.1007/978-3-031-10536-4_46

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  • Print ISBN: 978-3-031-10535-7

  • Online ISBN: 978-3-031-10536-4

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