Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India

Research Article

Research Frontiers in Sequential Pattern Mining

Download334 downloads
  • @INPROCEEDINGS{10.4108/eai.16-4-2022.2318239,
        author={Ritika  Ritika and Sunil Kumar Gupta},
        title={Research Frontiers in Sequential Pattern Mining},
        proceedings={Proceedings of The International Conference on Emerging Trends in Artificial Intelligence and Smart Systems, THEETAS 2022, 16-17 April 2022, Jabalpur, India},
        publisher={EAI},
        proceedings_a={THEETAS},
        year={2022},
        month={6},
        keywords={utility sequential pattern mining utility mining},
        doi={10.4108/eai.16-4-2022.2318239}
    }
    
  • Ritika Ritika
    Sunil Kumar Gupta
    Year: 2022
    Research Frontiers in Sequential Pattern Mining
    THEETAS
    EAI
    DOI: 10.4108/eai.16-4-2022.2318239
Ritika Ritika1,*, Sunil Kumar Gupta2
  • 1: I. K. Gujral Punjab Technical University, Kapurthala, Punjab, India
  • 2: Sardar Beant Singh State University, Gurdaspur, Punjab, India
*Contact email: ritikasood1987@gmail.com

Abstract

The area of sequential pattern mining is progressing rapidly day by day. The aim of sequential pattern mining is to extract subsequences satisfying a threshold parameter value. Researchers are addressing the challenges and problems occurring in various domains of sequential pattern mining(SPM) by defining new constraints, algorithms to increase the quality of patterns. This paper presents an organized study on the research in the varied domains of sequential pattern mining such as Traditional SPM, Time-interval SPM, High Utility SPM and High Utility Hierarchical SPM. The fields are classified on the basis of support, time or utility as the threshold parameter. A taxonomy of the popular algorithms in these diversified fields of sequential pattern mining is also presented. The paper aims to act as a valuable guide for researchers by suggesting future potentials in sequential pattern mining.