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An Analysis on Association Rule Mining Techniques

Published on March 2013 by Ish Nath Jha, Samarjeet Borah
journal_cover_thumbnail

International Conference on Computing, Communication and Sensor Network
Foundation of Computer Science USA
CCSN2012 - Number 4
March 2013
Authors: Ish Nath Jha, Samarjeet Borah
ae6b95ad-171e-44e2-8d6f-0c51185f5def

Ish Nath Jha, Samarjeet Borah . An Analysis on Association Rule Mining Techniques. International Conference on Computing, Communication and Sensor Network. CCSN2012, 4 (March 2013), 40-45.

@article{
author = { Ish Nath Jha, Samarjeet Borah },
title = { An Analysis on Association Rule Mining Techniques },
journal = { International Conference on Computing, Communication and Sensor Network },
issue_date = { March 2013 },
volume = { CCSN2012 },
number = { 4 },
month = { March },
year = { 2013 },
issn = 0975-8887,
pages = { 40-45 },
numpages = 6,
url = { /specialissues/ccsn2012/number4/11278-1043/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Special Issue Article
%1 International Conference on Computing, Communication and Sensor Network
%A Ish Nath Jha
%A Samarjeet Borah
%T An Analysis on Association Rule Mining Techniques
%J International Conference on Computing, Communication and Sensor Network
%@ 0975-8887
%V CCSN2012
%N 4
%P 40-45
%D 2013
%I International Journal of Computer Applications
Abstract

Association rule mining is a subfield of Data mining. It is a popular and widely used method to extract interesting and useful patterns from large sets of data. The first Rule Mining Algorithm was formulated by R. Agrawal in 1993. After the Apriori Algorithm formulated by R. Agrawal, many other algorithms have been proposed. Each of these algorithms has its own advantages and disadvantages over the others. The major issues of concern are the cost efficiency in terms of memory utilization, interestingness of the rules generated, influence of the minimum support level specified on the rules generated, the ability to discover relationships not only quantitatively but also qualitatively and the processing efficiency of the algorithm. This paper provides a comparative analysis on the classical Apriori algorithm along with some other association rule mining algorithms.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Association Rule Mining Support Confidence Apriori AIS FP-Tree