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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| Format: | Thesis |
| Language: | en en |
| Published: |
2007
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| Online Access: | https://etd.uum.edu.my/58/1/abdalla_ali.pdf https://etd.uum.edu.my/58/2/abdalla_ali.pdf https://etd.uum.edu.my/58/ |
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| _version_ | 1833435308936396800 |
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| author | Bala, Abdalla Ali Abdalla |
| author_facet | Bala, Abdalla Ali Abdalla |
| author_sort | Bala, Abdalla Ali Abdalla |
| building | UUM Library |
| collection | Institutional Repository |
| content_provider | Universiti Utara Malaysia |
| content_source | UUM Electronic Theses |
| continent | Asia |
| country | Malaysia |
| 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 |
| id | my.uum.etd-58 |
| institution | Universiti Utara Malaysia |
| language | en en |
| publishDate | 2007 |
| record_format | eprints |
| spelling | my.uum.etd-582013-07-24T12:05:27Z https://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 https://etd.uum.edu.my/58/1/abdalla_ali.pdf application/pdf en https://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. |
| spellingShingle | Q Science (General) Bala, Abdalla Ali Abdalla Features Reduction In Case Retrieval For Diabetes Dataset. |
| title | 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_short | Features Reduction In Case Retrieval For Diabetes Dataset. |
| title_sort | features reduction in case retrieval for diabetes dataset. |
| topic | Q Science (General) |
| url | https://etd.uum.edu.my/58/1/abdalla_ali.pdf https://etd.uum.edu.my/58/2/abdalla_ali.pdf https://etd.uum.edu.my/58/ |
| url_provider | http://etd.uum.edu.my/ |