|Author||Mabroukeh, Nizar R. ♦ Ezeife, C. I.|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|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.|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||ACM Computing Surveys (CSUR)|
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