On a mathematical relationship between the fixed point and the closed itemset in association rule mining

Tian Rui Li; Da Ruan; Tianmin Huang; Yang Xu

doi:10.1007/11589990_126

On a mathematical relationship between the fixed point and the closed itemset in association rule mining

Tian Rui Li, Da Ruan, Tianmin Huang, Yang Xu

Research output › peer-review

Abstract

Association rule mining is one of important research topics in knowledge discovery and data mining. Recent promising direction of association rule mining is mainly to mine closed itemsets. Based on the Galois closed operators, a mathematical relationship between the fixed point and closed itemset in association rule mining is discussed and several properties are obtained. To mine all frequent closed itemsets is equal to build the fixed point lattice and mine its all points that satisfy support constraints. A new method for visualization of association rules based on the generalized association rule base is also proposed.

Original language	English
Title of host publication	AI 2005
Subtitle of host publication	Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings
Place of Publication	Sydney, Australia
Publisher	Springer Verlag
Pages	989-992
Number of pages	4
ISBN (Print)	3540304622, 9783540304623
DOIs	https://doi.org/10.1007/11589990_126
State	Published - 2005
Event	AUS-Ai 2005 - 18th Australian Joint Conference on Artificial Intelligence - Sydney Duration: 5 Dec 2005 → 9 Dec 2005

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	3809 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	AUS-Ai 2005 - 18th Australian Joint Conference on Artificial Intelligence
Country/Territory	Australia
City	Sydney
Period	2005-12-05 → 2005-12-09

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/11589990_126License: Unspecified

Cite this

Li, T. R., Ruan, D., Huang, T., & Xu, Y. (2005). On a mathematical relationship between the fixed point and the closed itemset in association rule mining. In AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings (pp. 989-992). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3809 LNAI). Springer Verlag. https://doi.org/10.1007/11589990_126

Li, Tian Rui ; Ruan, Da ; Huang, Tianmin et al. / On a mathematical relationship between the fixed point and the closed itemset in association rule mining. AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. Sydney, Australia : Springer Verlag, 2005. pp. 989-992 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Association rule mining is one of important research topics in knowledge discovery and data mining. Recent promising direction of association rule mining is mainly to mine closed itemsets. Based on the Galois closed operators, a mathematical relationship between the fixed point and closed itemset in association rule mining is discussed and several properties are obtained. To mine all frequent closed itemsets is equal to build the fixed point lattice and mine its all points that satisfy support constraints. A new method for visualization of association rules based on the generalized association rule base is also proposed.",

keywords = "Association rules, Data mining",

author = "Li, {Tian Rui} and Da Ruan and Tianmin Huang and Yang Xu",

note = "Score = 2; AUS-Ai 2005 - 18th Australian Joint Conference on Artificial Intelligence ; Conference date: 05-12-2005 Through 09-12-2005",

year = "2005",

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language = "English",

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Li, TR, Ruan, D, Huang, T & Xu, Y 2005, On a mathematical relationship between the fixed point and the closed itemset in association rule mining. in AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3809 LNAI, Springer Verlag, Sydney, Australia, pp. 989-992, AUS-Ai 2005 - 18th Australian Joint Conference on Artificial Intelligence, Sydney, Australia, 2005-12-05. https://doi.org/10.1007/11589990_126

On a mathematical relationship between the fixed point and the closed itemset in association rule mining. / Li, Tian Rui; Ruan, Da; Huang, Tianmin et al.
AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. Sydney, Australia: Springer Verlag, 2005. p. 989-992 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3809 LNAI).

Research output › peer-review

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AB - Association rule mining is one of important research topics in knowledge discovery and data mining. Recent promising direction of association rule mining is mainly to mine closed itemsets. Based on the Galois closed operators, a mathematical relationship between the fixed point and closed itemset in association rule mining is discussed and several properties are obtained. To mine all frequent closed itemsets is equal to build the fixed point lattice and mine its all points that satisfy support constraints. A new method for visualization of association rules based on the generalized association rule base is also proposed.

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Li TR, Ruan D, Huang T, Xu Y. On a mathematical relationship between the fixed point and the closed itemset in association rule mining. In AI 2005: Advances in Artificial Intelligence - 18th Australian Joint Conference on Artificial Intelligence, Proceedings. Sydney, Australia: Springer Verlag. 2005. p. 989-992. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/11589990_126