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Interpretations of Discovered Knowledge in Multidimensional Databases

Li, Yuefeng (2007) Interpretations of Discovered Knowledge in Multidimensional Databases. In IEEE International Conference on Granular Computing, 2007. GRC 2007., 2-4 November 2007, San Jose, USA.

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Abstract

It is a big challenge to guarantee the quality of discovered knowledge in multidimensional databases because of the huge amount of patterns and noises. The essential issue is to provide efficient methods for interpreting meaningful discovered knowledge in databases. This research presents a new technique called granule mining to improve the performance of data mining. Rather than using patterns, it uses granules in different tiers to generalize knowledge in databases. It also provides a mechanism to formally discuss meaningless discovered rules based on relationships between granules in different tiers.

Impact and interest:

2 citations in Scopus
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1 citations in Web of Science®

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ID Code: 14291
Item Type: Conference Paper
DOI: 10.1109/GrC.2007.92
ISBN: 9780769530321
Subjects: Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > INFORMATION SYSTEMS (080600) > Information Systems Development Methodologies (080608)
Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING (080100) > Artificial Intelligence and Image Processing not elsewhere classified (080199)
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2007 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 05 Aug 2008
Last Modified: 29 Feb 2012 23:38

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