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Rough Set Applications for the Classification of Software Industries using Rule based Approach

by Ratnakar Das, Deepti Mishra, Sujogya Mishra
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 175 - Number 2
Year of Publication: 2017
Authors: Ratnakar Das, Deepti Mishra, Sujogya Mishra
10.5120/ijca2017915492

Ratnakar Das, Deepti Mishra, Sujogya Mishra . Rough Set Applications for the Classification of Software Industries using Rule based Approach. International Journal of Computer Applications. 175, 2 ( Oct 2017), 26-28. DOI=10.5120/ijca2017915492

@article{ 10.5120/ijca2017915492,
author = { Ratnakar Das, Deepti Mishra, Sujogya Mishra },
title = { Rough Set Applications for the Classification of Software Industries using Rule based Approach },
journal = { International Journal of Computer Applications },
issue_date = { Oct 2017 },
volume = { 175 },
number = { 2 },
month = { Oct },
year = { 2017 },
issn = { 0975-8887 },
pages = { 26-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume175/number2/28461-2017915492/ },
doi = { 10.5120/ijca2017915492 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:23:59.414856+05:30
%A Ratnakar Das
%A Deepti Mishra
%A Sujogya Mishra
%T Rough Set Applications for the Classification of Software Industries using Rule based Approach
%J International Journal of Computer Applications
%@ 0975-8887
%V 175
%N 2
%P 26-28
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Rough Set theory is a very handy tool for imprecise and vague pattern of data . This paper shows how the concept of RST being used in deriving information from hidden pattern of data . From large data base software industries are the object of interest for applying Rough Set concept on the collected data. The set of rules which have been derived will be helpful in the development of software industries. This paper has used two types of techniques in finding the reduct, first one uses cluster in finding different dissimilar groups the other one is the application of quick reduct algorithm in deriving the rules verifying them by using strength.

References
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  2. Z. Pawlak, Rough Sets, International Journal of Computer and Information Sciences, 11 (1982) 341–356.
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  4. Z. Pawlak, Decision Rules, Bayes_ rule and Rough Sets, in: N. Zhong, A. Skowron, S. Ohsuga (Eds.), New Direction in Rough Sets, Data Mining, and Granular-Soft Computing, Springer, Berlin, 1999, pp. 1–9
  5. L. Polkowski, A. Skowron (Eds.), “Rough Sets and Current Trends in Computing”, Lecture Notes in Artificial Intelligence, 1424, Springer, Berlin, 1998
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  8. N. Zhong, A. Skowron, S. Ohsuga (Eds.), New Direction in Rough Sets Data Mining and Granular-Soft Computing, Springer, Berlin, 1999.
  9. Renu Vashist M.L.Garg “Rule Generation based on Reduct and Core:A Rough Set Approach”,vol-29 no-9 IJCA 2011
Index Terms

Computer Science
Information Sciences

Keywords

RST-Rough Set Theory

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