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A Rough Set Approach for Generation and Validation of Rules for Missing Attribute Values of a Data Set

by Renu Vashist, M. L. Garg
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 42 - Number 14
Year of Publication: 2012
Authors: Renu Vashist, M. L. Garg
10.5120/5762-7938

Renu Vashist, M. L. Garg . A Rough Set Approach for Generation and Validation of Rules for Missing Attribute Values of a Data Set. International Journal of Computer Applications. 42, 14 ( March 2012), 31-35. DOI=10.5120/5762-7938

@article{ 10.5120/5762-7938,
author = { Renu Vashist, M. L. Garg },
title = { A Rough Set Approach for Generation and Validation of Rules for Missing Attribute Values of a Data Set },
journal = { International Journal of Computer Applications },
issue_date = { March 2012 },
volume = { 42 },
number = { 14 },
month = { March },
year = { 2012 },
issn = { 0975-8887 },
pages = { 31-35 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume42/number14/5762-7938/ },
doi = { 10.5120/5762-7938 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T20:32:11.257792+05:30
%A Renu Vashist
%A M. L. Garg
%T A Rough Set Approach for Generation and Validation of Rules for Missing Attribute Values of a Data Set
%J International Journal of Computer Applications
%@ 0975-8887
%V 42
%N 14
%P 31-35
%D 2012
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Data mining has emerged as most significant and continuously evolving field of research because of it's ever growing and far reaching applications into various areas such as medical, military, financial markets, banking etc. One of the most useful applications of data mining is extracting significant and earlier unknown knowledge from real-world databases. This knowledge may be in the form of rules. 'Rule generation' from raw data is a very effective and most widely used tool of data mining. Real life data are frequently imperfect, erroneous, incomplete, uncertain and vague. There are so many approaches for handling missing attribute values. In this paper we use the most common attribute value approach i. e. replacing all the missing attribute values by most frequently occurring attribute value and thereby completing the information table. Subsequently, we find the reduct and core of the complete decision table and verify that the reduct and core find by our method is same as the reduct and core find by ROSE2 software. Thereafter we generate the rules based on reduct. Our results are validated by conducting the same rough set analysis on the incomplete information system using the software ROSE2.

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Index Terms

Computer Science
Information Sciences

Keywords

Data Mining Knowledge Discovery From Database Machine Learning Reduct Core Missing Attribute Values Rule Generation

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