Rough clustering for web transactions

Grouping web transactions into clusters is important in order to obtain better understanding of user's behavior. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. It is based on the similarity of upper approximations of tran...

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Main Author:	Yanto, Iwan Tri Riyadi
Format:	Thesis
Language:	English English English
Published:	2011
Subjects:	QA Mathematics QA76 Computer software
Online Access:	http://eprints.uthm.edu.my/2629/1/24p%20IWAN%20TRI%20RIYADI%20YANTO.pdf http://eprints.uthm.edu.my/2629/2/IWAN%20TRI%20RIYADI%20YANTO%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2629/3/IWAN%20TRI%20RIYADI%20YANTO%20WATERMARK.pdf
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id	my-uthm-ep.2629
record_format	uketd_dc
spelling	my-uthm-ep.26292021-11-01T03:31:31Z Rough clustering for web transactions 2011-01 Yanto, Iwan Tri Riyadi QA Mathematics QA76 Computer software Grouping web transactions into clusters is important in order to obtain better understanding of user's behavior. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. It is based on the similarity of upper approximations of transactions by given threshold. However, the processing time is still an issue due to the high complexity for finding the similarity of upper approximations of a transaction which used to merge between two or more clusters. In this study, an alternative technique for grouping web transactions using rough set theory is proposed. It is based on the two similarity classes which is nonvoid intersection. The technique is implemented in MATLAB ® version 7.6.0.324 (R2008a). The two UCI benchmark datasets taken from: http:/kdd.ics.uci.edu/ databases/msnbc/msnbc.html and http:/kdd.ics.uci.edu/databases/ Microsoft / microsoft.html are opted in the simulation processes. The simulation reveals that the proposed technique significantly requires lower response time up to 62.69 % and 66.82 % as compared to the rough approximation-based clustering, severally. Meanwhile, for cluster purity it performs better until 2.5 % and 14.47%, respectively. 2011-01 Thesis http://eprints.uthm.edu.my/2629/ http://eprints.uthm.edu.my/2629/1/24p%20IWAN%20TRI%20RIYADI%20YANTO.pdf text en public http://eprints.uthm.edu.my/2629/2/IWAN%20TRI%20RIYADI%20YANTO%20COPYRIGHT%20DECLARATION.pdf text en staffonly http://eprints.uthm.edu.my/2629/3/IWAN%20TRI%20RIYADI%20YANTO%20WATERMARK.pdf text en validuser mphil masters Universiti Tun Hussein Malaysia Fakulti Sains Komputer dan Teknologi Maklumat
institution	Universiti Tun Hussein Onn Malaysia
collection	UTHM Institutional Repository
language	English English English
topic	QA Mathematics QA76 Computer software
spellingShingle	QA Mathematics QA76 Computer software Yanto, Iwan Tri Riyadi Rough clustering for web transactions
description	Grouping web transactions into clusters is important in order to obtain better understanding of user's behavior. Currently, the rough approximation-based clustering technique has been used to group web transactions into clusters. It is based on the similarity of upper approximations of transactions by given threshold. However, the processing time is still an issue due to the high complexity for finding the similarity of upper approximations of a transaction which used to merge between two or more clusters. In this study, an alternative technique for grouping web transactions using rough set theory is proposed. It is based on the two similarity classes which is nonvoid intersection. The technique is implemented in MATLAB ® version 7.6.0.324 (R2008a). The two UCI benchmark datasets taken from: http:/kdd.ics.uci.edu/ databases/msnbc/msnbc.html and http:/kdd.ics.uci.edu/databases/ Microsoft / microsoft.html are opted in the simulation processes. The simulation reveals that the proposed technique significantly requires lower response time up to 62.69 % and 66.82 % as compared to the rough approximation-based clustering, severally. Meanwhile, for cluster purity it performs better until 2.5 % and 14.47%, respectively.
format	Thesis
qualification_name	Master of Philosophy (M.Phil.)
qualification_level	Master's degree
author	Yanto, Iwan Tri Riyadi
author_facet	Yanto, Iwan Tri Riyadi
author_sort	Yanto, Iwan Tri Riyadi
title	Rough clustering for web transactions
title_short	Rough clustering for web transactions
title_full	Rough clustering for web transactions
title_fullStr	Rough clustering for web transactions
title_full_unstemmed	Rough clustering for web transactions
title_sort	rough clustering for web transactions
granting_institution	Universiti Tun Hussein Malaysia
granting_department	Fakulti Sains Komputer dan Teknologi Maklumat
publishDate	2011
url	http://eprints.uthm.edu.my/2629/1/24p%20IWAN%20TRI%20RIYADI%20YANTO.pdf http://eprints.uthm.edu.my/2629/2/IWAN%20TRI%20RIYADI%20YANTO%20COPYRIGHT%20DECLARATION.pdf http://eprints.uthm.edu.my/2629/3/IWAN%20TRI%20RIYADI%20YANTO%20WATERMARK.pdf
_version_	1747830970025771008

Rough clustering for web transactions

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