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Performance Evaluation on State of the Art Sequential Pattern Mining Algorithms

by Thomas Rincy. N, Yogadhar Pandey
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 65 - Number 14
Year of Publication: 2013
Authors: Thomas Rincy. N, Yogadhar Pandey
10.5120/10990-6157

Thomas Rincy. N, Yogadhar Pandey . Performance Evaluation on State of the Art Sequential Pattern Mining Algorithms. International Journal of Computer Applications. 65, 14 ( March 2013), 8-15. DOI=10.5120/10990-6157

@article{ 10.5120/10990-6157,
author = { Thomas Rincy. N, Yogadhar Pandey },
title = { Performance Evaluation on State of the Art Sequential Pattern Mining Algorithms },
journal = { International Journal of Computer Applications },
issue_date = { March 2013 },
volume = { 65 },
number = { 14 },
month = { March },
year = { 2013 },
issn = { 0975-8887 },
pages = { 8-15 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume65/number14/10990-6157/ },
doi = { 10.5120/10990-6157 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:18:47.075087+05:30
%A Thomas Rincy. N
%A Yogadhar Pandey
%T Performance Evaluation on State of the Art Sequential Pattern Mining Algorithms
%J International Journal of Computer Applications
%@ 0975-8887
%V 65
%N 14
%P 8-15
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining refers to extracting or mining knowledge from large amounts of data. Among the various data mining tasks sequential pattern mining is one of the most important tasks. It has broad applications in several domains such as the analysis of customer purchase patterns, web access patterns, seismologic data, and weather observations. Sequential pattern mining consists of mining subsequences that appear frequently in a set of sequences. Sequential pattern mining was first introduced by Rakesh Agarwal and Ramakrishnan Srikant in 1995. Many novel approaches for sequential pattern mining were proposed like Apriori, AprioriALL, GSP, SPADE, SPAM and PrefixSpan. In this paper, the performance of state-of-the-art sequential pattern mining algorithms PrefixSpan and SPAM is evaluated. "From the comprehensive experiments what have been done several phenomena were observed which are different from the traditional standpoint will be explained in this paper. "

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Sequential Pattern Mining PrefixSpan SPAM

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