Article

The Top-k frequent closed itemset mining using Top-k SAT problem

Authors:

Yakoub SalhiAuthors Info & Claims

ECMLPKDD'13: Proceedings of the 2013th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part III

Pages 403 - 418

https://doi.org/10.1007/978-3-642-40994-3_26

Published: 23 September 2013 Publication History

Abstract

In this paper, we introduce a new problem, called Top-k SAT, that consists in enumerating the Top-k models of a propositional formula. A Top-k model is defined as a model with less than k models preferred to it with respect to a preference relation. We show that Top-k SAT generalizes two well-known problems: the partial Max-SAT problem and the problem of computing minimal models. Moreover, we propose a general algorithm for Top-k SAT. Then, we give the first application of our declarative framework in data mining, namely, the problem of enumerating the Top-k frequent closed itemsets of length at least min (FCIM^k_min). Finally, to show the nice declarative aspects of our framework, we encode several other variants of FCIM^k_min into the Top-k SAT problem.

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Cited By

Jabbour SEl Mazouri FSais L(2018)Mining Negatives Association Rules Using ConstraintsProcedia Computer Science10.1016/j.procs.2018.01.146127:C(481-488)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1016/j.procs.2018.01.146
Benhamou B(2017)Local and global symmetry breaking in itemset miningAnnals of Mathematics and Artificial Intelligence10.1007/s10472-016-9528-480:1(91-112)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s10472-016-9528-4
Boudane AJabbour SSais LSalhi Y(2016)A SAT-based approach for mining association rulesProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060832.3060967(2472-2478)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060832.3060967
Show More Cited By

The Top-k frequent closed itemset mining using Top-k SAT problem
1. Information systems
  1. Information systems applications

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Published In

cover image Guide Proceedings

ECMLPKDD'13: Proceedings of the 2013th European Conference on Machine Learning and Knowledge Discovery in Databases - Volume Part III

September 2013

685 pages

ISBN:9783642409936

Editors:
Hendrik Blockeel
Department of Computer Science, Katholieke Universiteit Leuven, Leuven, Belgium
,
Kristian Kersting
Fraunhofer IAIS, Department of Knowledge Discovery, Schloss Birlinghoven, University of Bonn, Sankt Augustin, Germany
,
Siegfried Nijssen
LIACS, Universiteit Leiden, Leiden, The Netherlands and KU Leuven, Department of Computer Science, Leuven, Belgium
,
Filip Železný
Department of Computer Science and Engineering, Czech Technical University, Prague 6, Czech Republic

Sponsors

XRCE: Xerox Research Centre Europe
Winton Capital Management: Winton Capital Management
Cisco Systems
Yahoo! Labs
CSKI: Czech Society for Cybernetics and Informatics

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Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 23 September 2013

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Cited By

Jabbour SEl Mazouri FSais L(2018)Mining Negatives Association Rules Using ConstraintsProcedia Computer Science10.1016/j.procs.2018.01.146127:C(481-488)Online publication date: 1-May-2018
https://dl.acm.org/doi/10.1016/j.procs.2018.01.146
Benhamou B(2017)Local and global symmetry breaking in itemset miningAnnals of Mathematics and Artificial Intelligence10.1007/s10472-016-9528-480:1(91-112)Online publication date: 1-May-2017
https://dl.acm.org/doi/10.1007/s10472-016-9528-4
Boudane AJabbour SSais LSalhi Y(2016)A SAT-based approach for mining association rulesProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060832.3060967(2472-2478)Online publication date: 9-Jul-2016
https://dl.acm.org/doi/10.5555/3060832.3060967
Jabbour SRaddaoui BSais LSalhi Y(2016)On the computation of top-k extensions in abstract argumentation frameworksProceedings of the Twenty-second European Conference on Artificial Intelligence10.3233/978-1-61499-672-9-913(913-920)Online publication date: 29-Aug-2016
https://dl.acm.org/doi/10.3233/978-1-61499-672-9-913

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