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Reseach Article

User Interactive PostProcessing of Association Rules and Correlation based Redundancy Removal

Published on April 2012 by C. Sweetlin, V. Kalaivani
International Conference in Recent trends in Computational Methods, Communication and Controls
Foundation of Computer Science USA
ICON3C - Number 3
April 2012
Authors: C. Sweetlin, V. Kalaivani
14e5ff1b-aab7-4516-a893-b8dfb0ee7e91

C. Sweetlin, V. Kalaivani . User Interactive PostProcessing of Association Rules and Correlation based Redundancy Removal. International Conference in Recent trends in Computational Methods, Communication and Controls. ICON3C, 3 (April 2012), 31-35.

@article{
author = { C. Sweetlin, V. Kalaivani },
title = { User Interactive PostProcessing of Association Rules and Correlation based Redundancy Removal },
journal = { International Conference in Recent trends in Computational Methods, Communication and Controls },
issue_date = { April 2012 },
volume = { ICON3C },
number = { 3 },
month = { April },
year = { 2012 },
issn = 0975-8887,
pages = { 31-35 },
numpages = 5,
url = { /proceedings/icon3c/number3/6022-1023/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference in Recent trends in Computational Methods, Communication and Controls
%A C. Sweetlin
%A V. Kalaivani
%T User Interactive PostProcessing of Association Rules and Correlation based Redundancy Removal
%J International Conference in Recent trends in Computational Methods, Communication and Controls
%@ 0975-8887
%V ICON3C
%N 3
%P 31-35
%D 2012
%I International Journal of Computer Applications
Abstract

Traditional association rule mining generates a large number of rules. This leads to a difficulty in finding the interested and significant rules. An efficient interactive post-processing task which includes ontology and rule schema is used to obtain user interesting rules. Correlation analysis finds significant association rules by analyzing the dependency between the antecedent and consequent parts of the rule. In this paper, correlation analysis is integrated with the interactive post-processing to obtain significant user interesting rules. A redundancy removal follows this framework to weed out the extra rules and also to reduce the ruleset further. The proposed methodology provides a significant set of non-redundant user interesting rules leading to an efficient analysis

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Index Terms

Computer Science
Information Sciences

Keywords

Postprocessing User Knowledge Ontology Rule Schema Correlation Redundant Rules