Efficient Web Usage Mining Process for Sequential Patterns

Nguyen, TTS

Efficient Web Usage Mining Process for Sequential Patterns

Nguyen, TTS

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Publisher:: ACM
Publication Type:: Conference
Citation:: Nguyen Thi 2009, 'Efficient Web Usage Mining Process for Sequential Patterns', ACM, Malaysia, pp. 455-459.
Issue Date:: 2009

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Field	Value	Language
dc.contributor.author	Nguyen, TTS	en_US
dc.contributor.editor	Kotsis, G.; Taniar, D.; Pardede, E.; & Khalil, I.	en_US
dc.date	20091214	en_US
dc.date.accessioned	2010-06-16T05:00:00Z
dc.date.available	2010-06-16T05:00:00Z
dc.date.issued	2009	en_US
dc.identifier	2009001632	en_US
dc.identifier.citation	Nguyen Thi 2009, 'Efficient Web Usage Mining Process for Sequential Patterns', ACM, Malaysia, pp. 455-459.	en_US
dc.identifier.issn	978-1-60558-660-1	en_US
dc.identifier.other	E1	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11917
dc.description.abstract	The tremendous growth in volume of web usage data results in the boost of web mining research with focus on discovering potentially useful knowledge from web usage data. This paper presents a new web usage mining process for finding sequential patterns in web usage data which can be used for predicting the possible next move in browsing sessions for web personalization. This process consists of three main stages: preprocessing web access sequences from the web server log, mining preprocessed web log access sequences by a tree-based algorithm, and predicting web access sequences by using a dynamic clustering-based model. It is designed based on the integration of the dynamic clustering-based Markov model with the Pre-Order Linked WAP-Tree Mining (PLWAP) algorithm to enhance mining performance. The proposed mining process is verified by experiments with promising results.	en_US
dc.publisher	ACM	en_US
dc.relation.ispartof	Verified OK	en_US
dc.relation.ispartof	The 11th International Conference on Information Integration and Web-based Applications & Services	en_US
dc.relation.hasversion	Accepted manuscript version	en_US
dc.relation.isbasedon	NA	en_US
dc.rights	© ACM 2009. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in iiWAS2009, The 11th International Conference on Information Integration and Web-based Applications & Services (2009) http://www.iiwas.org/conferences/iiwas2009/	en_US
dc.subject	Markov Model, Sequential Patterns, Web Access Patterns (WAP), Pre-Order Linked WAP-Tree (PLWAP-tree), Web Usage Mining (WUM)	en_US
dc.subject.classification	Information Retrieval and Web Search	en_US
dc.title	Efficient Web Usage Mining Process for Sequential Patterns	en_US
dc.type	Conference
utslib.location	Malaysia	en_US
utslib.citation.startpage	455	en_US
utslib.citation.endpage	459	en_US
utslib.identifier.org	FEIT.Faculty of Engineering & Information Technology	en_US
utslib.for	080704	en_US
utslib.percentage	100	en_US
dc.location.activity	Kuala Lumpur, Malaysia	en_US
utslib.copyright.status	open_access
pubs.declined	1970-01-01T00:00:00.0+1000

Abstract:

The tremendous growth in volume of web usage data results in the boost of web mining research with focus on discovering potentially useful knowledge from web usage data. This paper presents a new web usage mining process for finding sequential patterns in web usage data which can be used for predicting the possible next move in browsing sessions for web personalization. This process consists of three main stages: preprocessing web access sequences from the web server log, mining preprocessed web log access sequences by a tree-based algorithm, and predicting web access sequences by using a dynamic clustering-based model. It is designed based on the integration of the dynamic clustering-based Markov model with the Pre-Order Linked WAP-Tree Mining (PLWAP) algorithm to enhance mining performance. The proposed mining process is verified by experiments with promising results.

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http://hdl.handle.net/10453/11917