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Applied Computing and Intelligence

2023, Volume 3, Issue 1: 44-78. doi: 10.3934/aci.2023004
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From basic approaches to novel challenges and applications in Sequential Pattern Mining

  • 1.
    Dept. of Information Engineering, University of Pisa, Italy; alessio.bechini@unipi.it, pietro.delloglio@unifi.it, francesco.marcelloni@unipi.it
  • 2.
    Dept. of Computer Science, University of Pisa, Italy; alessandro.bondielli@unipi.it
  • 3.
    Dept. of Philology, Literature and Lingustics, University of Pisa, Italy; alessandro.bondielli@unipi.it
  • Received: 14 September 2022 Revised: 05 January 2023 Accepted: 11 January 2023 Published: 06 February 2023

  • Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding statistically relevant regularities of patterns in sequentially ordered data. It has been an active area of research since mid 1990s. Even if many prime algorithms for SPM have a long history, the field is nevertheless very active. The literature is focused on novel challenges and applications, and on the development of more efficient and effective algorithms. In this paper, we present a brief overview on the landscape of algorithms for SPM, including an evaluation on performances for some of them. Further, we explore additional problems that have spanned from SPM. Finally, we evaluate available resources for SPM, and hypothesize on future directions for the field.

    Citation: Alessio Bechini, Alessandro Bondielli, Pietro Dell'Oglio, Francesco Marcelloni. From basic approaches to novel challenges and applications in Sequential Pattern Mining[J]. Applied Computing and Intelligence, 2023, 3(1): 44-78. doi: 10.3934/aci.2023004

    Related Papers:

  • Sequential Pattern Mining (SPM) is a branch of data mining that deals with finding statistically relevant regularities of patterns in sequentially ordered data. It has been an active area of research since mid 1990s. Even if many prime algorithms for SPM have a long history, the field is nevertheless very active. The literature is focused on novel challenges and applications, and on the development of more efficient and effective algorithms. In this paper, we present a brief overview on the landscape of algorithms for SPM, including an evaluation on performances for some of them. Further, we explore additional problems that have spanned from SPM. Finally, we evaluate available resources for SPM, and hypothesize on future directions for the field.


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