From basic approaches to novel challenges and applications in Sequential Pattern Mining-Reference-Cited by-同舟云学术

From basic approaches to novel challenges and applications in Sequential Pattern Mining

Published:2023 Issue:1 Volume:3 Page:44-78
ISSN:2688-1594
Container-title:Electronic Research Archive
language:
Short-container-title:era

Author:

Bechini Alessio¹,Bondielli Alessandro²³,Dell'Oglio Pietro¹,Marcelloni Francesco¹

Affiliation:

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

Abstract

<abstract><p>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.</p></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

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