2.50
Hdl Handle:
http://hdl.handle.net/10547/333865
Title:
Concurrent sequential patterns mining and frequent partial orders modelling
Authors:
Lu, Jing; Keech, Malcolm; Chen, Weiru; Wang, Cuiqing
Abstract:
Structural relation patterns have been introduced to extend the search for complex patterns often hidden behind large sequences of data, with applications (e.g.) in the analysis of customer behaviour, bioinformatics and web mining. In the overall context of frequent itemset mining, the focus of attention in the structural relation patterns family has been on the mining of concurrent sequential patterns, where a companion approach to graph-based modelling can be illuminating. The crux of this paper sets out to establish the connection between concurrent sequential patterns and frequent partial orders, which are well known for discovering ordering information from sequence databases. It is shown that frequent partial orders can be derived from concurrent sequential patterns, under certain conditions, and worked examples highlight the relationship. Experiments with real and synthetic datasets contrast the results of the data mining and modelling involved.
Affiliation:
University of Bedfordshire
Citation:
Lu, J., Keech, M., Chen, W., Wang, C. (2013) 'Concurrent sequential patterns mining and frequent partial orders modelling' International Journal of Business Intelligence and Data Mining 8 (2):132
Publisher:
Inderscience Publishers
Journal:
International Journal of Business Intelligence and Data Mining
Issue Date:
2013
URI:
http://hdl.handle.net/10547/333865
DOI:
10.1504/IJBIDM.2013.057751
Additional Links:
http://www.inderscience.com/link.php?id=57751
Type:
Article
Language:
en
ISSN:
1743-8187; 1743-8195
Appears in Collections:
Centre for Research in Distributed Technologies (CREDIT)

Full metadata record

DC FieldValue Language
dc.contributor.authorLu, Jingen
dc.contributor.authorKeech, Malcolmen
dc.contributor.authorChen, Weiruen
dc.contributor.authorWang, Cuiqingen
dc.date.accessioned2014-11-10T10:28:47Z-
dc.date.available2014-11-10T10:28:47Z-
dc.date.issued2013-
dc.identifier.citationLu, J., Keech, M., Chen, W., Wang, C. (2013) 'Concurrent sequential patterns mining and frequent partial orders modelling' International Journal of Business Intelligence and Data Mining 8 (2):132en
dc.identifier.issn1743-8187-
dc.identifier.issn1743-8195-
dc.identifier.doi10.1504/IJBIDM.2013.057751-
dc.identifier.urihttp://hdl.handle.net/10547/333865-
dc.description.abstractStructural relation patterns have been introduced to extend the search for complex patterns often hidden behind large sequences of data, with applications (e.g.) in the analysis of customer behaviour, bioinformatics and web mining. In the overall context of frequent itemset mining, the focus of attention in the structural relation patterns family has been on the mining of concurrent sequential patterns, where a companion approach to graph-based modelling can be illuminating. The crux of this paper sets out to establish the connection between concurrent sequential patterns and frequent partial orders, which are well known for discovering ordering information from sequence databases. It is shown that frequent partial orders can be derived from concurrent sequential patterns, under certain conditions, and worked examples highlight the relationship. Experiments with real and synthetic datasets contrast the results of the data mining and modelling involved.en
dc.language.isoenen
dc.publisherInderscience Publishersen
dc.relation.urlhttp://www.inderscience.com/link.php?id=57751en
dc.rightsArchived with thanks to International Journal of Business Intelligence and Data Miningen
dc.subjectsequential patterns post-processingen
dc.subjectstructural relation patternsen
dc.subjectSRPen
dc.subjectconcurrent sequential patterns miningen
dc.subjectfrequent partial orders modellingen
dc.subjectknowledge discoveryen
dc.titleConcurrent sequential patterns mining and frequent partial orders modellingen
dc.typeArticleen
dc.contributor.departmentUniversity of Bedfordshireen
dc.identifier.journalInternational Journal of Business Intelligence and Data Miningen
All Items in UOBREP are protected by copyright, with all rights reserved, unless otherwise indicated.