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Comparative Study of Various Sequential Pattern Mining Algorithms

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International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 90 - Number 17
Year of Publication: 2014
Authors:
Nidhi Grover
10.5120/15815-4703

Nidhi Grover. Article: Comparative Study of Various Sequential Pattern Mining Algorithms. International Journal of Computer Applications 90(17):36-41, March 2014. Full text available. BibTeX

@article{key:article,
	author = {Nidhi Grover},
	title = {Article: Comparative Study of Various Sequential Pattern Mining Algorithms},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {90},
	number = {17},
	pages = {36-41},
	month = {March},
	note = {Full text available}
}

Abstract

In Sequential pattern mining represents an important class of data mining problems with wide range of applications. It is one of the very challenging problems because it deals with the careful scanning of a combinatorially large number of possible subsequence patterns. Broadly sequential pattern ming algorithms can be classified into three types namely Apriori based approaches, Pattern growth algorithms and Early pruning algorithms. These algorithms have further classification and extensions. Detailed explanation of each algorithm along with its important features, pseudo code, advantages and disadvantages is given in the subsequent sections of the paper. At the end a comparative analysis of all the algorithms with their supporting features is given in the form of a table. This paper tries to enrich the knowledge and understanding of various approaches of sequential pattern mining.

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