AOG-ags Algorithms and Applications

Lizhen Wang, Junli Lu, Joan Lu, Jim Yip

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationAdvanced Data Mining and Applications
Subtitle of host publicationThird International Conference, ADMA 2007, Proceedings
EditorsReda Alhajj, Hong Gao, Jianzhong Li, Xue Li, Osmar R. Zaïane
Number of pages12
ISBN (Electronic)9783540738718
ISBN (Print)9783540738701
Publication statusPublished - 10 Sep 2007
Event3rd International Conference on Advanced Data Mining and Applications - Harbin, China
Duration: 6 Aug 20078 Aug 2007
Conference number: 3

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume4632 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference3rd International Conference on Advanced Data Mining and Applications
Abbreviated titleADMA 2007
Internet address


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