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Rule Generation based on Reduct and Core: A Rough Set Approach

by Renu Vashist, Prof. M.L Garg
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 29 - Number 9
Year of Publication: 2011
Authors: Renu Vashist, Prof. M.L Garg
10.5120/3595-4989

Renu Vashist, Prof. M.L Garg . Rule Generation based on Reduct and Core: A Rough Set Approach. International Journal of Computer Applications. 29, 9 ( September 2011), 1-5. DOI=10.5120/3595-4989

@article{ 10.5120/3595-4989,
author = { Renu Vashist, Prof. M.L Garg },
title = { Rule Generation based on Reduct and Core: A Rough Set Approach },
journal = { International Journal of Computer Applications },
issue_date = { September 2011 },
volume = { 29 },
number = { 9 },
month = { September },
year = { 2011 },
issn = { 0975-8887 },
pages = { 1-5 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume29/number9/3595-4989/ },
doi = { 10.5120/3595-4989 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:15:44.296665+05:30
%A Renu Vashist
%A Prof. M.L Garg
%T Rule Generation based on Reduct and Core: A Rough Set Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 29
%N 9
%P 1-5
%D 2011
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rough set theory has evolved as one of the most important technique used for feature selection as a result of contemporary developments in data mining. One of the cardinal uses of Rough set theory is its application for rule generation. More often attribute reduction poses a major challenge for developing the theory and applications of rough set. This paper proposes a unique mathematical approach for determining the most important attribute with the help of confidence and strength of an association. Our approach focuses on the elimination of the redundant attributes in order to generate the effective reduct set (i.e., reduced set of necessary attributes) and formulating the core of the attribute set. Subsequently, only a subset of feature is selected which retain the accuracy of the original features.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Rule generation Rough set Knowledge Discovery Reduct Core

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