Discovering attributes dependency for categorical data set based on soft set theory for better decision making

Attribute dependency concludes the association between attributes for better accurate decision making. However, the task involved in identifying the relation between categorical values in data set is a complex process. This main focus of this paper is to determine the attribute dependency in a real...

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Main Authors: Mohd Isa, Awang, Ahmad Nazari, Mohd Rose, Fadhilah, Ahmad
Format: Article
Language:English
Published: Hikari Ltd. 2015
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Online Access:http://eprints.unisza.edu.my/7137/1/FH02-FIK-16-06428.jpg
http://eprints.unisza.edu.my/7137/
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spelling my-unisza-ir.71372022-09-13T04:44:14Z http://eprints.unisza.edu.my/7137/ Discovering attributes dependency for categorical data set based on soft set theory for better decision making Mohd Isa, Awang Ahmad Nazari, Mohd Rose Fadhilah, Ahmad QA75 Electronic computers. Computer science Attribute dependency concludes the association between attributes for better accurate decision making. However, the task involved in identifying the relation between categorical values in data set is a complex process. This main focus of this paper is to determine the attribute dependency in a real world application. The proposed method is based on the notion of mapping inclusion from the soft set theory. The categorical data is transformed to predicate and value set to discover the dependency among the attributes. The result shows that the attribute dependencies obtained are comparable to the rough set approach. Hikari Ltd. 2015 Article PeerReviewed image en http://eprints.unisza.edu.my/7137/1/FH02-FIK-16-06428.jpg Mohd Isa, Awang and Ahmad Nazari, Mohd Rose and Fadhilah, Ahmad (2015) Discovering attributes dependency for categorical data set based on soft set theory for better decision making. Applied Mathematical Sciences, 9 (130). pp. 6477-6490. ISSN 1312885X [P]
institution Universiti Sultan Zainal Abidin
building UNISZA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Sultan Zainal Abidin
content_source UNISZA Institutional Repository
url_provider https://eprints.unisza.edu.my/
language English
topic QA75 Electronic computers. Computer science
spellingShingle QA75 Electronic computers. Computer science
Mohd Isa, Awang
Ahmad Nazari, Mohd Rose
Fadhilah, Ahmad
Discovering attributes dependency for categorical data set based on soft set theory for better decision making
description Attribute dependency concludes the association between attributes for better accurate decision making. However, the task involved in identifying the relation between categorical values in data set is a complex process. This main focus of this paper is to determine the attribute dependency in a real world application. The proposed method is based on the notion of mapping inclusion from the soft set theory. The categorical data is transformed to predicate and value set to discover the dependency among the attributes. The result shows that the attribute dependencies obtained are comparable to the rough set approach.
format Article
author Mohd Isa, Awang
Ahmad Nazari, Mohd Rose
Fadhilah, Ahmad
author_facet Mohd Isa, Awang
Ahmad Nazari, Mohd Rose
Fadhilah, Ahmad
author_sort Mohd Isa, Awang
title Discovering attributes dependency for categorical data set based on soft set theory for better decision making
title_short Discovering attributes dependency for categorical data set based on soft set theory for better decision making
title_full Discovering attributes dependency for categorical data set based on soft set theory for better decision making
title_fullStr Discovering attributes dependency for categorical data set based on soft set theory for better decision making
title_full_unstemmed Discovering attributes dependency for categorical data set based on soft set theory for better decision making
title_sort discovering attributes dependency for categorical data set based on soft set theory for better decision making
publisher Hikari Ltd.
publishDate 2015
url http://eprints.unisza.edu.my/7137/1/FH02-FIK-16-06428.jpg
http://eprints.unisza.edu.my/7137/
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score 13.159267