AOG-ags Algorithms and Applications

Lizhen Wang, Junli Lu, Joan Lu, Jim Yip

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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.

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
PublisherSpringer
Pages323-334
Number of pages12
Volume4632
Edition1
ISBN (Electronic)9783540738718
ISBN (Print)9783540738701
DOIs
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
https://dl.acm.org/doi/proceedings/10.5555/1421171

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

Conference

Conference3rd International Conference on Advanced Data Mining and Applications
Abbreviated titleADMA 2007
CountryChina
CityHarbin
Period6/08/078/08/07
Internet address

Fingerprint

Attribute
Trees (mathematics)
Data mining
Generalization
Expand
Optimization Algorithm
Data Mining
Partition
Equivalence
Experimental Results

Cite this

Wang, L., Lu, J., Lu, J., & Yip, J. (2007). AOG-ags Algorithms and Applications. In R. Alhajj, H. Gao, J. Li, X. Li, & O. R. Zaïane (Eds.), Advanced Data Mining and Applications : Third International Conference, ADMA 2007, Proceedings (1 ed., Vol. 4632, pp. 323-334). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4632 LNAI). Springer. https://doi.org/10.1007/978-3-540-73871-8_30
Wang, Lizhen ; Lu, Junli ; Lu, Joan ; Yip, Jim. / AOG-ags Algorithms and Applications. Advanced Data Mining and Applications : Third International Conference, ADMA 2007, Proceedings. editor / Reda Alhajj ; Hong Gao ; Jianzhong Li ; Xue Li ; Osmar R. Zaïane. Vol. 4632 1. ed. Springer, 2007. pp. 323-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).
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title = "AOG-ags Algorithms and Applications",
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), Attributes' generalization sequences (AGS), Concept hierarchy trees, Equivalence partition trees, Interestingness of AGS",
author = "Lizhen Wang and Junli Lu and Joan Lu and Jim Yip",
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Wang, L, Lu, J, Lu, J & Yip, J 2007, AOG-ags Algorithms and Applications. in R Alhajj, H Gao, J Li, X Li & OR Zaïane (eds), Advanced Data Mining and Applications : Third International Conference, ADMA 2007, Proceedings. 1 edn, vol. 4632, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 4632 LNAI, Springer, pp. 323-334, 3rd International Conference on Advanced Data Mining and Applications, Harbin, China, 6/08/07. https://doi.org/10.1007/978-3-540-73871-8_30

AOG-ags Algorithms and Applications. / Wang, Lizhen; Lu, Junli; Lu, Joan; Yip, Jim.

Advanced Data Mining and Applications : Third International Conference, ADMA 2007, Proceedings. ed. / Reda Alhajj; Hong Gao; Jianzhong Li; Xue Li; Osmar R. Zaïane. Vol. 4632 1. ed. Springer, 2007. p. 323-334 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 4632 LNAI).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Wang L, Lu J, Lu J, Yip J. AOG-ags Algorithms and Applications. In Alhajj R, Gao H, Li J, Li X, Zaïane OR, editors, Advanced Data Mining and Applications : Third International Conference, ADMA 2007, Proceedings. 1 ed. Vol. 4632. Springer. 2007. p. 323-334. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). https://doi.org/10.1007/978-3-540-73871-8_30