An analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title

Document clustering has been investigated for use in a number of different areas of information retrieval. In this project, the use of Fuzzy clustering techniques for suggestion of supervisors and examiners of thesis in School of Postgraduate Studies at Faculty of Computer Science and Information Te...

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Main Author:	Suhaimi, Azrina
Format:	Thesis
Language:	English
Published:	2005
Subjects:	QA75 Electronic computers Computer science
Online Access:	http://eprints.utm.my/id/eprint/2709/1/AzrinaSuhaimiMFC2005.pdf
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id	my-utm-ep.2709
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spelling	my-utm-ep.27092018-06-25T00:40:58Z An analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title 2005-11 Suhaimi, Azrina QA75 Electronic computers. Computer science Document clustering has been investigated for use in a number of different areas of information retrieval. In this project, the use of Fuzzy clustering techniques for suggestion of supervisors and examiners of thesis in School of Postgraduate Studies at Faculty of Computer Science and Information Technology are studied. The aim of this project is to assist the administration in assigning supervisors and examiners to each post graduate student for their project. Preprocessing tasks for document clustering that are applied in this project are commonly used in the Information Retrieval field, which are stemming, stopword removal, and indexing. Document is represented using the Vector Space Model. The index terms are then clustered using Fuzzy clustering algorithms based on similarity. The selected algorithms for Fuzzy are Fuzzy C-means and Gustafson Kessel. The clustering results are evaluated in terms of classification accuracy to predict the thesis supervisor(s) or examiner(s). Experiments show that Fuzzy C-means gives better result compared to Gustafson Kessel. However, the performances of both techniques are not at the top level. Hence, these techniques are not suitable for use in suggestion of supervisors and examiners. Nevertheless, to get a better performance, a larger dataset, thorough experiments and detailed evaluation has to be carried out and this will take longer time 2005-11 Thesis http://eprints.utm.my/id/eprint/2709/ http://eprints.utm.my/id/eprint/2709/1/AzrinaSuhaimiMFC2005.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	QA75 Electronic computers Computer science
spellingShingle	QA75 Electronic computers Computer science Suhaimi, Azrina An analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title
description	Document clustering has been investigated for use in a number of different areas of information retrieval. In this project, the use of Fuzzy clustering techniques for suggestion of supervisors and examiners of thesis in School of Postgraduate Studies at Faculty of Computer Science and Information Technology are studied. The aim of this project is to assist the administration in assigning supervisors and examiners to each post graduate student for their project. Preprocessing tasks for document clustering that are applied in this project are commonly used in the Information Retrieval field, which are stemming, stopword removal, and indexing. Document is represented using the Vector Space Model. The index terms are then clustered using Fuzzy clustering algorithms based on similarity. The selected algorithms for Fuzzy are Fuzzy C-means and Gustafson Kessel. The clustering results are evaluated in terms of classification accuracy to predict the thesis supervisor(s) or examiner(s). Experiments show that Fuzzy C-means gives better result compared to Gustafson Kessel. However, the performances of both techniques are not at the top level. Hence, these techniques are not suitable for use in suggestion of supervisors and examiners. Nevertheless, to get a better performance, a larger dataset, thorough experiments and detailed evaluation has to be carried out and this will take longer time
format	Thesis
qualification_level	Master's degree
author	Suhaimi, Azrina
author_facet	Suhaimi, Azrina
author_sort	Suhaimi, Azrina
title	An analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title
title_short	An analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title
title_full	An analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title
title_fullStr	An analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title
title_full_unstemmed	An analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title
title_sort	analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title
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/2709/1/AzrinaSuhaimiMFC2005.pdf
_version_	1747814426560430080

An analysis of fuzzy clustering algortihms for suggestion of supervisor and examiner of thesis title

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