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Reseach Article

Mining Multiple Level Association Rules to Mining Multiple Level Correlations to discover Complex Patterns

by Mamta, Shwetank Arya, R. P. Agarwal
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 58 - Number 22
Year of Publication: 2012
Authors: Mamta, Shwetank Arya, R. P. Agarwal
10.5120/9430-3528

Mamta, Shwetank Arya, R. P. Agarwal . Mining Multiple Level Association Rules to Mining Multiple Level Correlations to discover Complex Patterns. International Journal of Computer Applications. 58, 22 ( November 2012), 19-24. DOI=10.5120/9430-3528

@article{ 10.5120/9430-3528,
author = { Mamta, Shwetank Arya, R. P. Agarwal },
title = { Mining Multiple Level Association Rules to Mining Multiple Level Correlations to discover Complex Patterns },
journal = { International Journal of Computer Applications },
issue_date = { November 2012 },
volume = { 58 },
number = { 22 },
month = { November },
year = { 2012 },
issn = { 0975-8887 },
pages = { 19-24 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume58/number22/9430-3528/ },
doi = { 10.5120/9430-3528 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:03:12.128520+05:30
%A Mamta
%A Shwetank Arya
%A R. P. Agarwal
%T Mining Multiple Level Association Rules to Mining Multiple Level Correlations to discover Complex Patterns
%J International Journal of Computer Applications
%@ 0975-8887
%V 58
%N 22
%P 19-24
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Recently, frequent pattern mining (FPM) has become one of the most popular data mining approaches for various applications such as education, medical, farming, analysis of sale and purchase patterns etc. Apriori algorithm [11] and FP growth algorithm are working efficiently in data mining. These algorithms are typically restricted to a single concept level of hierarchy and uniform support threshold. Sometimes domain database support concept hierarchies that represent the relationships among many different concept levels. In this paper efforts are made to discover items at multiple levels of concept hierarchy. Up till now, a very few concern has been shown to this area. In this study mining multiple levels is explored and extended to mining cross levels in large database on the basis of user specified reduced support threshold constraint.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Complex patterns multiple level association rules cross level association rules IDIV