AOG-ags Algorithms and Applications

  • Lizhen Wang
  • Junli Lu
  • Joan Lu
  • Jim Yip
Conference paper

DOI: 10.1007/978-3-540-73871-8_30

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4632)
Cite this paper as:
Wang L., Lu J., Lu J., Yip J. (2007) AOG-ags Algorithms and Applications. In: Alhajj R., Gao H., Li J., Li X., Zaïane O.R. (eds) Advanced Data Mining and Applications. ADMA 2007. Lecture Notes in Computer Science, vol 4632. Springer, Berlin, Heidelberg

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.

Keywords

Attribute-oriented generalization (AOG) Concept hierarchy trees Attributes’ generalization sequences (AGS) Equivalence partition trees Interestingness of AGS 

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Lizhen Wang
    • 1
    • 2
  • Junli Lu
    • 1
  • Joan Lu
    • 2
  • Jim Yip
    • 2
  1. 1.Department of Computer Science and Engineering, School of Information, Yunnan, University, Kunming, 650091P.R. China
  2. 2.Department of Informatics, School of Computing and Engineering, University of, Huddersfield, Huddersfield, HD1 3DHUK

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