Discovering diverse association rules from multidimensional schema

Simple item page
aut.relation.endpage5996
aut.relation.issue15
aut.relation.pages22
aut.relation.startpage5975
aut.relation.volume40
aut.researcherPears, Russel Lawrence
dc.contributor.authorUsman, M
dc.contributor.authorPears R
dc.contributor.authorFong, A
dc.contributor.editorLin, B
dc.date.accessioned2013-06-09T22:42:36Z
dc.date.accessioned2013-06-11T02:59:48Z
dc.date.accessioned2013-06-11T03:00:07Z
dc.date.accessioned2013-06-11T03:02:15Z
dc.date.accessioned2013-06-11T03:03:36Z
dc.date.available2013-06-09T22:42:36Z
dc.date.available2013-06-11T02:59:48Z
dc.date.available2013-06-11T03:00:07Z
dc.date.available2013-06-11T03:02:15Z
dc.date.available2013-06-11T03:03:36Z
dc.date.copyright2013-11-01
dc.date.issued2013-11-01
dc.description.abstractThe integration of data mining techniques with data warehousing is gaining popularity due to the fact that both disciplines complement each other in extracting knowledge from large datasets. However, the majority of approaches focus on applying data mining as a front end technology to mine data warehouses. Surprisingly, little progress has been made in incorporating mining techniques in the design of data warehouses. While methods such as data clustering applied on multidimensional data have been shown to enhance the knowledge discovery process, a number of fundamental issues remain unresolved with respect to the design of multidimensional schema. These relate to automated support for the selection of informative dimension and fact variables in high dimensional and data intensive environments, an activity which may challenge the capabilities of human designers on account of the sheer scale of data volume and variables involved. In this research, we propose a methodology that selects a subset of informative dimension and fact variables from an initial set of candidates. Our experimental results conducted on three real world datasets taken from the UCI machine learning repository show that the knowledge discovered from the schema that we generated was more diverse and informative than the standard approach of mining the original data without the use of our multidimensional structure imposed on it.
dc.identifier.citationExpert Systems with Applications, vol.40(15), pp.5975 - 5996 (22)
dc.identifier.doi10.1016/j.eswa.2013.05.031
dc.identifier.urihttps://hdl.handle.net/10292/5460
dc.publisherElsevier
dc.relation.replaceshttp://hdl.handle.net/10292/5436
dc.relation.replaces10292/5436
dc.relation.replaceshttp://hdl.handle.net/10292/5457
dc.relation.replaces10292/5457
dc.relation.replaceshttp://hdl.handle.net/10292/5458
dc.relation.replaces10292/5458
dc.relation.replaceshttp://hdl.handle.net/10292/5459
dc.relation.replaces10292/5459
dc.relation.urihttp://dx.doi.org/10.1016/j.eswa.2013.05.031
dc.rightsCopyright © 2013 Elsevier Ltd. All rights reserved. This is the author’s version of a work that was accepted for publication in (see Citation). Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. The definitive version was published in (see Citation). The original publication is available at (see Publisher's Version).
dc.rights.accessrightsOpenAccess
dc.subjectAssociation rules
dc.subjectData cubes
dc.subjectKnowledge discovery
dc.subjectOLAP analysis
dc.subjectMultidimensional schema
dc.titleDiscovering diverse association rules from multidimensional schema
dc.typeJournal Article
pubs.elements-id149142
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/School of Computing & Mathematical Science
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