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Author Mabroukeh, Nizar R. ♦ Ezeife, C. I.
Source ACM Digital Library
Content type Text
Publisher Association for Computing Machinery (ACM)
File Format PDF
Copyright Year ©2010
Language English
Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Data mining ♦ Web usage mining ♦ Apriori property ♦ Association rules ♦ Early pruning ♦ Frequent patterns ♦ Lattice theory ♦ Lexicographic order ♦ Pattern growth ♦ Prediction ♦ Recommender systems ♦ Sequence mining ♦ Sequential patterns ♦ Tree projection ♦ Web log
Abstract Owing to important applications such as mining web page traversal sequences, many algorithms have been introduced in the area of sequential pattern mining over the last decade, most of which have also been modified to support concise representations like closed, maximal, incremental or hierarchical sequences. This article presents a taxonomy of sequential pattern-mining techniques in the literature with web usage mining as an application. This article investigates these algorithms by introducing a taxonomy for classifying sequential pattern-mining algorithms based on important key features supported by the techniques. This classification aims at enhancing understanding of sequential pattern-mining problems, current status of provided solutions, and direction of research in this area. This article also attempts to provide a comparative performance analysis of many of the key techniques and discusses theoretical aspects of the categories in the taxonomy.
ISSN 03600300
Age Range 18 to 22 years ♦ above 22 year
Educational Use Research
Education Level UG and PG
Learning Resource Type Article
Publisher Date 2010-12-03
Publisher Place New York
e-ISSN 15577341
Journal ACM Computing Surveys (CSUR)
Volume Number 43
Issue Number 1
Page Count 41
Starting Page 1
Ending Page 41


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Source: ACM Digital Library