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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Main Author:	Mohd. Nasir, Nurul Nisa
Format:	Thesis
Language:	English
Published:	2005
Subjects:	QA76 Computer software
Online Access:	http://eprints.utm.my/id/eprint/3600/1/NurulNisaMohdMFSKSM2005.pdf
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id	my-utm-ep.3600
record_format	uketd_dc
spelling	my-utm-ep.36002018-01-07T08:19:32Z An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners 2005-11 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 Wardâ€™s algorithm is better for suggestion supervisor and examiner compared to Kohonen network. 2005-11 Thesis http://eprints.utm.my/id/eprint/3600/ http://eprints.utm.my/id/eprint/3600/1/NurulNisaMohdMFSKSM2005.pdf application/pdf en public masters Universiti Teknologi Malaysia, Faculty of Computer Science and Information System Faculty of Computer Science and Information System
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA76 Computer software
spellingShingle	QA76 Computer software Mohd. Nasir, Nurul Nisa An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners
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 Wardâ€™s algorithm is better for suggestion supervisor and examiner compared to Kohonen network.
format	Thesis
qualification_level	Master's degree
author	Mohd. Nasir, Nurul Nisa
author_facet	Mohd. Nasir, Nurul Nisa
author_sort	Mohd. Nasir, Nurul Nisa
title	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_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_sort	analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners
granting_institution	Universiti Teknologi Malaysia, Faculty of Computer Science and Information System
granting_department	Faculty of Computer Science and Information System
publishDate	2005
url	http://eprints.utm.my/id/eprint/3600/1/NurulNisaMohdMFSKSM2005.pdf
_version_	1747814459511930880

An analysis of hierarchical clustering and neural network clustering for suggestion supervisors and examiners

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