Article

Association reducts: a framework for mining multi-attribute dependencies

Author:

Dominik ŚlezakAuthors Info & Claims

ISMIS'05: Proceedings of the 15th international conference on Foundations of Intelligent Systems

Pages 354 - 363

https://doi.org/10.1007/11425274_37

Published: 25 May 2005 Publication History

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Abstract

We introduce the notion of an association reduct. It is an analogy to association rules at the level of global dependencies between the sets of attributes. Association reducts represent important complex relations, beyond usually considered “single attribute – single attribute” similarities. They can also express approximate dependencies in terms of, for instance, the information-theoretic measures. Finally, association reducts can be extracted from data using algorithms adapted from the domain of association rules and the theory of rough sets.

References

[1]

Agrawal, R., Mannila, H., Srikant, R., Toivonen, H., Verkamo, A.I.: Fast discovery of assocation rules. In: Advances in Knowledge Discovery and Data Mining. AAAI/MIT Press (1996) pp. 307-328.

Digital Library

Google Scholar

[2]

Bazan, J., Nguyen, H.S., Nguyen, S.H, Synak, P., Wróblewski, J.: Rough Set Algorithms in Classification Problem. In: Rough Set Methods and Applications. Physica Verlag (2000) pp. 49-88.

Crossref

Google Scholar

[3]

Kapur, J.N., Kesavan, H.K.: Entropy Optimization Principles with Applications. Academic Press (1992).

Google Scholar

[4]

Kloesgen, W., Z? ytkow, J.M. (eds): Handbook of Data Mining and Knowledge Discovery. Oxford University Press (2002).

Digital Library

Google Scholar

[5]

Nguyen, H.S., Nguyen S.H.: Rough Sets and Association Rule Generation. Fundamenta Informaticae 40/4, IOS Press (1999) pp. 310-318.

Digital Library

Google Scholar

[6]

Nguyen, S.H., Nguyen H.S.: Pattern extraction from data. Fundamenta Informaticae 34/1-2, IOS Press (1998) pp. 129-144.

Digital Library

Google Scholar

[7]

Pawlak, Z.: Rough sets - Theoretical aspects of reasoning about data. Kluwer Academic Publishers (1991).

Digital Library

Google Scholar

[8]

Phan-Luong, V.: The Representative Basis for Association Rules. In: Proc. of ICDM'2001. San Jose, California (2001) pp. 639-640.

Digital Library

Google Scholar

[9]

Ślezak, D.: Approximate Entropy Reducts. Fundamenta Informaticae 53/3-4, IOS Press (2002) pp. 365-390.

Digital Library

Google Scholar

[10]

Ślezak, D., Wróblewski, J.: Order-based genetic algorithms for the search of approximate entropy reducts. In: Proc. of RSFDGrC'2003. Chongqing, China (2003).

Digital Library

Google Scholar

Cited By

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Yao YFu R(2013)The concept of reducts in pawlak three-step rough set analysisTransactions on Rough Sets XVI10.5555/2554673.2554677(53-72)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.5555/2554673.2554677
Puri CKumar N(2011)Projected Gustafson-Kessel clustering algorithm and its convergenceTransactions on rough sets XIV10.5555/2017701.2017710(159-182)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.5555/2017701.2017710
Moshkov MSkowron ASuraj Z(2010)Irreducible descriptive sets of attributes for information systemsTransactions on Rough Sets XI10.5555/2126376.2126382(92-105)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.5555/2126376.2126382
Show More Cited By

Association reducts: a framework for mining multi-attribute dependencies
1. Information systems
  1. Information systems applications

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Information

Published In

ISMIS'05: Proceedings of the 15th international conference on Foundations of Intelligent Systems

May 2005

697 pages

ISBN:3540258787

Editors:
Mohand-Said Hacid
LIRIS - UFR d'Informatique, Université Claude Bernard Lyon 1, 43, boulevard du 11 novembre 1918, Villeurbanne, France
,
Neil V. Murray
Department of Computer Science, State University of New York, 43, boulevard du 11 novembre 1918, Albany, NY, France
,
Zbigniew W. Raś
Department of Computer Science, University of North Carolina, 43, boulevard du 11 novembre 1918, Charlotte, NC, France
,
Shusaku Tsumoto
Department of Computer Science, Shimane University, 89-1 Enya-cho Izumo, Shimane, NC, Japan

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Springer-Verlag

Berlin, Heidelberg

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Published: 25 May 2005

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Yao YFu R(2013)The concept of reducts in pawlak three-step rough set analysisTransactions on Rough Sets XVI10.5555/2554673.2554677(53-72)Online publication date: 1-Jan-2013
https://dl.acm.org/doi/10.5555/2554673.2554677
Puri CKumar N(2011)Projected Gustafson-Kessel clustering algorithm and its convergenceTransactions on rough sets XIV10.5555/2017701.2017710(159-182)Online publication date: 1-Jan-2011
https://dl.acm.org/doi/10.5555/2017701.2017710
Moshkov MSkowron ASuraj Z(2010)Irreducible descriptive sets of attributes for information systemsTransactions on Rough Sets XI10.5555/2126376.2126382(92-105)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.5555/2126376.2126382
Terlecki P(2010)On the relation between jumping emerging patterns and rough set theory with application to data classificationTransactions on rough sets XII10.5555/1880429.1880442(236-338)Online publication date: 1-Jan-2010
https://dl.acm.org/doi/10.5555/1880429.1880442
Ślęzak D(2006)Association reductsProceedings of the 5th international conference on Rough Sets and Current Trends in Computing10.1007/11908029_18(157-164)Online publication date: 6-Nov-2006
https://dl.acm.org/doi/10.1007/11908029_18
Ślęzak D(2006)Association reductsProceedings of the First international conference on Rough Sets and Knowledge Technology10.1007/11795131_44(305-312)Online publication date: 24-Jul-2006
https://dl.acm.org/doi/10.1007/11795131_44

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Beyond intratransaction association analysis: mining multidimensional intertransaction association rules

Efficient Mining of Intertransaction Association Rules

Discovering association rules change from large databases

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