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dc.contributor.authorLu, Jingen
dc.contributor.authorKeech, Malcolmen
dc.contributor.authorWang, Cuiqingen
dc.date.accessioned2014-11-07T14:10:47Z
dc.date.available2014-11-07T14:10:47Z
dc.date.issued2013-08
dc.identifier.citationLu, J., Keech, M., Wang, C., French, T. (2013) 'Applications of Concurrent Access Patterns in Web Usage Mining' 15th International Conference on Data Warehousing and Knowledge Discovery, 26th-29th August 2013, Prague, Czech Republic.en
dc.identifier.isbn9783642401312
dc.identifier.doi10.1007/978-3-642-40131-2_30
dc.identifier.urihttp://hdl.handle.net/10547/333821
dc.description.abstractThis paper builds on the original data mining and modelling research which has proposed the discovery of novel structural relation patterns, applying the approach in web usage mining. The focus of attention here is on concurrent access patterns (CAP), where an overarching framework illuminates the methodology for web access patterns post-processing. Data pre-processing, pattern discovery and patterns analysis all proceed in association with access patterns mining, CAP mining and CAP modelling. Pruning and selection of access patterns takes place as necessary, allowing further CAP mining and modelling to be pursued in the search for the most interesting concurrent access patterns. It is shown that higher level CAPs can be modelled in a way which brings greater structure to bear on the process of knowledge discovery. Experiments with real-world datasets highlight the applicability of the approach in web navigation.
dc.language.isoenen
dc.publisherSpringeren
dc.relation.urlhttp://link.springer.com/chapter/10.1007/978-3-642-40131-2_30en
dc.subjectweb access patterns (WAP) post-processingen
dc.subjectconcurrent access patterns (CAP)en
dc.subjectCAP mining and modellingen
dc.subjectWAP pruningen
dc.subjectknowledge discoveryen
dc.subjectweb access patternsen
dc.titleApplications of concurrent access patterns in web usage miningen
dc.title.alternativeData warehousing and knowledge discoveryen
dc.typeConference papers, meetings and proceedingsen
dc.contributor.departmentUniversity of Bedfordshireen
dc.identifier.journalLecture notes in computer scienceen
html.description.abstractThis paper builds on the original data mining and modelling research which has proposed the discovery of novel structural relation patterns, applying the approach in web usage mining. The focus of attention here is on concurrent access patterns (CAP), where an overarching framework illuminates the methodology for web access patterns post-processing. Data pre-processing, pattern discovery and patterns analysis all proceed in association with access patterns mining, CAP mining and CAP modelling. Pruning and selection of access patterns takes place as necessary, allowing further CAP mining and modelling to be pursued in the search for the most interesting concurrent access patterns. It is shown that higher level CAPs can be modelled in a way which brings greater structure to bear on the process of knowledge discovery. Experiments with real-world datasets highlight the applicability of the approach in web navigation.


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