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, 4632/2 . Springer Berlin / Heidelberg, pp. 323-334. ISBN 9783-540738701Metadata only available from this repository.
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 > High-Performance Intelligent Computing
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School of Computing and Engineering > High-Performance Intelligent Computing > Information and Systems Engineering Group
|Depositing User:||Briony Heyhoe|
|Date Deposited:||27 Jun 2008 15:39|
|Last Modified:||09 Dec 2010 13:10|
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