• DocumentCode
    3762092
  • Title

    Survey on sequential pattern mining algorithms

  • Author

    Sedigheh Abbasghorbani;Reza Tavoli

  • Author_Institution
    Young Researchers and Elite Club, Chalus Branch, Islamic Azad University, Chalus, Iran
  • fYear
    2015
  • Firstpage
    1153
  • Lastpage
    1164
  • Abstract
    Because of the important applications in today´s world such as, users behavior in buying, mining web page traversal sequences or disease treatments, many algorithms have been produced in the area of sequential pattern mining over the last decade, most of which have also been modified to support short representations like closed, maximal, incremental or hierarchical sequences. This article reviews a number of algorithms in each category and puts them in taxonomy of sequential pattern mining techniques as an application. This article checks these algorithms by taxonomy for classifying sequential pattern mining algorithms based on their theoretical features and say advantage/disadvantage of them. This classification help to enhancing understanding of sequential pattern mining problems, current status of provided solutions, and direction of research in this area.
  • Keywords
    "Decision support systems","Data mining","Databases","DH-HEMTs","Iron"
  • Publisher
    ieee
  • Conference_Titel
    Knowledge-Based Engineering and Innovation (KBEI), 2015 2nd International Conference on
  • Type

    conf

  • DOI
    10.1109/KBEI.2015.7436211
  • Filename
    7436211