AOG-ags algorithms and applications

Wang, L; Lu, J; Yip, YJ; Lu, J

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.

Citation

Wang, L., Lu, J., Yip, Y., & Lu, J. (2007). AOG-ags algorithms and applications. In Advanced Data Mining and Applications (323-334). Springer Berlin / Heidelberg

• Publication Date: Aug 1, 2007
• Deposit Date: Aug 14, 2012
• Pages: 323-334
• Series Title: Lecture Notes in Computer Science
• Book Title: Advanced Data Mining and Applications
• ISBN: 9783-540738701
• Publisher URL: http://www.springerlink.com/content/42788r5274454u38/
• Additional Information: Additional Information : From the The Third International Conference on Advanced Data Mining and Applications, Harbin, China, August 6-8, 2007