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A Novel Progressive Sampling based Approach for Effective Mining of Association Rules

by V.Umarani, M.Punithavalli
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 10 - Number 9
Year of Publication: 2010
Authors: V.Umarani, M.Punithavalli
10.5120/1511-1796

V.Umarani, M.Punithavalli . A Novel Progressive Sampling based Approach for Effective Mining of Association Rules. International Journal of Computer Applications. 10, 9 ( November 2010), 15-18. DOI=10.5120/1511-1796

@article{ 10.5120/1511-1796,
author = { V.Umarani, M.Punithavalli },
title = { A Novel Progressive Sampling based Approach for Effective Mining of Association Rules },
journal = { International Journal of Computer Applications },
issue_date = { November 2010 },
volume = { 10 },
number = { 9 },
month = { November },
year = { 2010 },
issn = { 0975-8887 },
pages = { 15-18 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume10/number9/1511-1796/ },
doi = { 10.5120/1511-1796 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T19:58:35.163778+05:30
%A V.Umarani
%A M.Punithavalli
%T A Novel Progressive Sampling based Approach for Effective Mining of Association Rules
%J International Journal of Computer Applications
%@ 0975-8887
%V 10
%N 9
%P 15-18
%D 2010
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Mining Association Rules from huge databases is one of the important issue that need to be addressed. This paper presents a new sampling based association rule mining algorithm that uses a progressive sampling approach based on negative border and Frequent pattern growth (FP Growth) algorithm for finding the candidate item sets which ultimately shortens the execution time in generating the candidate itemsets. Experimental results reveals that the propsed approach is significantly more efficient than the Apriori based sampling approach.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Apriori Negative border FP-Growth Sampling Temporal Characteristics

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