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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
Metadata only available from this repository.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 |
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| 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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