An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners
Document clustering has been investigated for use in a number of different areas of information retrieval. This study applies hierarchical based document clustering and neural network based document clustering to suggest supervisors and examiners for thesis. The results of both techniques were compa...
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| Format: | Thesis |
| Language: | en |
| Published: |
2005
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| Online Access: | http://eprints.utm.my/3600/1/NurulNisaMohdMFSKSM2005.pdf http://eprints.utm.my/3600/ |
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| _version_ | 1845470932048543744 |
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| author | Mohd. Nasir, Nurul Nisa |
| author_facet | Mohd. Nasir, Nurul Nisa |
| author_sort | Mohd. Nasir, Nurul Nisa |
| building | UTM Library |
| collection | Institutional Repository |
| content_provider | Universiti Teknologi Malaysia |
| content_source | UTM Institutional Repository |
| continent | Asia |
| country | Malaysia |
| description | Document clustering has been investigated for use in a number of different areas of information retrieval. This study applies hierarchical based document clustering and neural network based document clustering to suggest supervisors and examiners for thesis. The results of both techniques were compared to the expert survey. The collection of 206 theses was used and employed the pre-processed using stopword removal and stemming. Inter document similarity were measured using Euclidean distance before clustering techniques were applied. The results show that Ward’s algorithm is better for suggestion supervisor and examiner compared to Kohonen network. |
| format | Thesis |
| id | my.utm.eprints-3600 |
| institution | Universiti Teknologi Malaysia |
| language | en |
| publishDate | 2005 |
| record_format | eprints |
| spelling | my.utm.eprints-36002018-01-07T08:19:32Z http://eprints.utm.my/3600/ An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners Mohd. Nasir, Nurul Nisa QA76 Computer software Document clustering has been investigated for use in a number of different areas of information retrieval. This study applies hierarchical based document clustering and neural network based document clustering to suggest supervisors and examiners for thesis. The results of both techniques were compared to the expert survey. The collection of 206 theses was used and employed the pre-processed using stopword removal and stemming. Inter document similarity were measured using Euclidean distance before clustering techniques were applied. The results show that Ward’s algorithm is better for suggestion supervisor and examiner compared to Kohonen network. 2005-11 Thesis NonPeerReviewed application/pdf en http://eprints.utm.my/3600/1/NurulNisaMohdMFSKSM2005.pdf Mohd. Nasir, Nurul Nisa (2005) An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners. Masters thesis, Universiti Teknologi Malaysia, Faculty of Computer Science and Information System. |
| spellingShingle | QA76 Computer software Mohd. Nasir, Nurul Nisa An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners |
| title | An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners |
| title_full | An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners |
| title_fullStr | An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners |
| title_full_unstemmed | An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners |
| title_short | An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners |
| title_sort | analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners |
| topic | QA76 Computer software |
| url | http://eprints.utm.my/3600/1/NurulNisaMohdMFSKSM2005.pdf http://eprints.utm.my/3600/ |
| url_provider | http://eprints.utm.my/ |