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AOG-ags algorithms and applications

Wang, Lizhen, Lu, Joan, Yip, Yau Jim and Lu, Joan (2007) AOG-ags algorithms and applications. In: Advanced Data Mining and Applications. Lecture Notes in Computer Science, 4632/2007 . Springer Berlin / Heidelberg, pp. 323-334. ISBN 9783-540738701

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Abstract

The attribute-oriented generalization (AOG for short) method is one of the most important data mining methods. In this paper, a reasonable approach of AOG (AOG-ags, attribute-oriented generalization based on attributes’ generalization sequence), which expands the traditional AOG method efficiently, is proposed. By introducing equivalence partition trees, an optimization algorithm of the AOG-ags is devised. Defining interestingness of attributes’ generalization sequences, the selection problem of attributes’ generalization sequences is solved. Extensive experimental results show that the AOG-ags are useful and efficient. Particularly, by using the AOG-ags algorithm in a plant distributing dataset, some distributing rules for the species of plants in an area are found interesting.

Item Type: Book Chapter
Additional Information: From the The Third International Conference on Advanced Data Mining and Applications, Harbin, China, August 6-8, 2007
Subjects: T Technology > T Technology (General)
T Technology > TA Engineering (General). Civil engineering (General)
Schools: School of Computing and Engineering
School of Computing and Engineering > Diagnostic Engineering Research Centre
School of Computing and Engineering > Diagnostic Engineering Research Centre > Measurement System and Signal Processing Research Group
School of Computing and Engineering > Informatics Research Group
School of Computing and Engineering > Informatics Research Group > Software Engineering Research Group
School of Computing and Engineering > Informatics Research Group > XML, Database and Information Retrieval Research Group
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Depositing User: Briony Heyhoe
Date Deposited: 27 Jun 2008 16:39
Last Modified: 09 Dec 2010 13:10
URI: http://eprints.hud.ac.uk/id/eprint/937

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