AOG-ags Algorithms and Applications

Lizhen Wang; Junli Lu; Joan Lu; Jim Yip

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

AOG-ags Algorithms and Applications

Lizhen Wang, Junli Lu, Joan Lu, Jim Yip

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 language	English
Title of host publication	Advanced Data Mining and Applications
Subtitle of host publication	Third International Conference, ADMA 2007, Proceedings
Editors	Reda Alhajj, Hong Gao, Jianzhong Li, Xue Li, Osmar R. Zaïane
Publisher	Springer
Pages	323-334
Number of pages	12
Volume	4632
Edition	1
ISBN (Electronic)	9783540738718
ISBN (Print)	9783540738701
DOIs	https://doi.org/10.1007/978-3-540-73871-8_30
Publication status	Published - 10 Sept 2007
Event	3rd International Conference on Advanced Data Mining and Applications - Harbin, China Duration: 6 Aug 2007 → 8 Aug 2007 Conference number: 3 https://dl.acm.org/doi/proceedings/10.5555/1421171

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	4632 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	3rd International Conference on Advanced Data Mining and Applications
Abbreviated title	ADMA 2007
Country/Territory	China
City	Harbin
Period	6/08/07 → 8/08/07
Internet address	https://dl.acm.org/doi/proceedings/10.5555/1421171

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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, & 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 et al. / 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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doi = "10.1007/978-3-540-73871-8_30",

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booktitle = "Advanced Data Mining and Applications",

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edition = "1",

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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 et al.
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 proceeding › Conference contribution › peer-review

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T1 - AOG-ags Algorithms and Applications

AU - Wang, Lizhen

AU - Lu, Junli

AU - Lu, Joan

AU - Yip, Jim

N1 - Conference code: 3

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Y1 - 2007/9/10

N2 - 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.

AB - 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.

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KW - Concept hierarchy trees

KW - Equivalence partition trees

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SN - 9783540738701

VL - 4632

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 323

EP - 334

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A2 - Gao, Hong

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A2 - Li, Xue

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ER -

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)). doi: 10.1007/978-3-540-73871-8_30

AOG-ags Algorithms and Applications

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