Data guided approach to generate multi-dimensional schema for targeted knowledge discovery

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aut.conference.typePaper Published in Proceedings
aut.researcherPears, Russel Lawrence
dark.contributor.authorUsman, M
dark.contributor.authorPears, RL
dark.contributor.authorFong, A
dc.contributor.authorPears, RL
dc.contributor.authorUsman, M
dc.contributor.authorFong, A
dc.contributor.editorChristen, P
dc.date.accessioned2013-02-26T04:40:08Z
dc.date.available2013-02-26T04:40:08Z
dc.date.copyright2012
dc.date.issued2012
dc.description.abstractData mining and data warehousing are two key technologies which have made significant contributions to the field of knowledge discovery in a variety of domains. More recently, the integrated use of traditional data mining techniques such as clustering and pattern recognition with data warehousing technique of Online Analytical Processing (OLAP) have motivated diverse research areas for leveraging knowledge discovery from complex real-world datasets. Recently, a number of such integrated methodologies have been proposed to extract knowledge from datasets but most of these methodologies lack automated and generic methods for schema generation and knowledge extraction. Mostly data analysts need to rely on domain specific knowledge and have to cope with technological constraints in order to discover knowledge from high dimensional datasets. In this paper we present a generic methodology which incorporates semi-automated knowledge extraction methods to provide data-driven assistance towards knowledge discovery. In particular, we provide a method for constructing a binary tree of hierarchical clusters and annotate each node in the tree with significant numeric variables. Additionally, we propose automated methods to rank nominal variables and to generate candidate multidimensional schema with highly significant dimensions. We have performed three case studies on three real-world datasets taken from the UCI machine learning repository in order to validate the generality and applicability of our proposed methodology.
dc.identifier.citationThe Tenth Australasian Data Mining Conference: AusDM 2012 held at Sydney, Australia
dc.identifier.urihttps://hdl.handle.net/10292/5189
dc.publisherAustralian Computer Society (ACS)
dc.relation.urihttp://ausdm12.togaware.com/accepted.html
dc.rightsCopyright © 2012, Australian Computer Society, Inc. This paper appeared at the 10th Australasian Data Mining Conference (AusDM 2012), Sydney, Australia, December 2012. Conferences in Research and Practice in Information Technology (CRPIT), Vol. 134, Yanchang Zhao, Jiuyong Li, Paul Kennedy, and Peter Christen, Ed. Reproduction for academic, not-for-profit purposes permitted provided this text is included.
dc.rights.accessrightsOpenAccess
dc.subjectData mining
dc.subjectData warehousing
dc.subjectSchema generation
dc.subjectKnowledge discovery
dc.titleData guided approach to generate multi-dimensional schema for targeted knowledge discovery
dc.typeConference Contribution
pubs.elements-id134060
pubs.organisational-data/AUT
pubs.organisational-data/AUT/Design & Creative Technologies
pubs.organisational-data/AUT/Design & Creative Technologies/School of Computing & Mathematical Science
pubs.organisational-data/AUT/PBRF Researchers
pubs.organisational-data/AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers
pubs.organisational-data/AUT/PBRF Researchers/Design & Creative Technologies PBRF Researchers/DCT C & M Computing
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