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Association Rule Mining Algorithm’s Variant Analysis

by Prince Verma, Dinesh Kumar
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 78 - Number 14
Year of Publication: 2013
Authors: Prince Verma, Dinesh Kumar
10.5120/13593-1366

Prince Verma, Dinesh Kumar . Association Rule Mining Algorithm’s Variant Analysis. International Journal of Computer Applications. 78, 14 ( September 2013), 26-34. DOI=10.5120/13593-1366

@article{ 10.5120/13593-1366,
author = { Prince Verma, Dinesh Kumar },
title = { Association Rule Mining Algorithm’s Variant Analysis },
journal = { International Journal of Computer Applications },
issue_date = { September 2013 },
volume = { 78 },
number = { 14 },
month = { September },
year = { 2013 },
issn = { 0975-8887 },
pages = { 26-34 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume78/number14/13593-1366/ },
doi = { 10.5120/13593-1366 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:51:35.656222+05:30
%A Prince Verma
%A Dinesh Kumar
%T Association Rule Mining Algorithm’s Variant Analysis
%J International Journal of Computer Applications
%@ 0975-8887
%V 78
%N 14
%P 26-34
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Association rule mining is a vital technique of data mining which is of great use and importance. For Association Rule mining various new techniques have been developed but concept for the newly developed algorithms remains the same. These recent developments are basically modifications to previous ones which introduce some minute changes in existing algorithms for better results. This paper addresses these basic algorithms and techniques in an elaborative way. So the reader can understand each of these techniques along with their pros and cons, without any additional effort. The techniques discussed here involve AIS, SETM, Apriori and FP-tree in detail.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining KDD Process Association Rule Mining Pruning

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