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
Link To Document :
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