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Features Reduction In Case Retrieval For Diabetes Dataset.

In reality, the organizations often have the great quantity of data stored in the databases. The large size of data in terms of the number of attributes and objects make the analysis process becomes very difficult as the data are complex. In order to overcome this problem, the use of sufficient numb...

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Main Author: Bala, Abdalla Ali Abdalla
Format: Thesis
Language:English
English
Published: 2007
Subjects:
Q Science (General)
Online Access:http://etd.uum.edu.my/58/1/abdalla_ali.pdf
http://etd.uum.edu.my/58/2/abdalla_ali.pdf
http://etd.uum.edu.my/58/
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spelling my.uum.etd.582013-07-24T12:05:27Z http://etd.uum.edu.my/58/ Features Reduction In Case Retrieval For Diabetes Dataset. Bala, Abdalla Ali Abdalla Q Science (General) In reality, the organizations often have the great quantity of data stored in the databases. The large size of data in terms of the number of attributes and objects make the analysis process becomes very difficult as the data are complex. In order to overcome this problem, the use of sufficient number of attributes and objects will contribute to get the best solution. There are many techniques which can be employed to reduce the number of attributes in the dataset. In this study, two techniques core using, namely rough set theory and Case-Based Reasoning were applied to the medical dataset. 2007-08 Thesis NonPeerReviewed application/pdf en http://etd.uum.edu.my/58/1/abdalla_ali.pdf application/pdf en http://etd.uum.edu.my/58/2/abdalla_ali.pdf Bala, Abdalla Ali Abdalla (2007) Features Reduction In Case Retrieval For Diabetes Dataset. Masters thesis, Universiti Utara Malaysia.
institution Universiti Utara Malaysia
building UUM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Utara Malaysia
content_source UUM Electronic Theses
url_provider http://etd.uum.edu.my/
language English
English
topic Q Science (General)
spellingShingle Q Science (General)
Bala, Abdalla Ali Abdalla
Features Reduction In Case Retrieval For Diabetes Dataset.
description In reality, the organizations often have the great quantity of data stored in the databases. The large size of data in terms of the number of attributes and objects make the analysis process becomes very difficult as the data are complex. In order to overcome this problem, the use of sufficient number of attributes and objects will contribute to get the best solution. There are many techniques which can be employed to reduce the number of attributes in the dataset. In this study, two techniques core using, namely rough set theory and Case-Based Reasoning were applied to the medical dataset.
format Thesis
author Bala, Abdalla Ali Abdalla
author_facet Bala, Abdalla Ali Abdalla
author_sort Bala, Abdalla Ali Abdalla
title Features Reduction In Case Retrieval For Diabetes Dataset.
title_short Features Reduction In Case Retrieval For Diabetes Dataset.
title_full Features Reduction In Case Retrieval For Diabetes Dataset.
title_fullStr Features Reduction In Case Retrieval For Diabetes Dataset.
title_full_unstemmed Features Reduction In Case Retrieval For Diabetes Dataset.
title_sort features reduction in case retrieval for diabetes dataset.
publishDate 2007
url http://etd.uum.edu.my/58/1/abdalla_ali.pdf
http://etd.uum.edu.my/58/2/abdalla_ali.pdf
http://etd.uum.edu.my/58/
_version_ 1644276066069512192
score 13.211869

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