Article

Bridging conjunctive and disjunctive search spaces for mining a new concise and exact representation of correlated patterns

Authors:

Nassima Ben Younes,

Tarek Hamrouni,

Sadok Ben YahiaAuthors Info & Claims

DS'10: Proceedings of the 13th international conference on Discovery science

Pages 189 - 204

Published: 06 October 2010 Publication History

Abstract

In the literature, many works were interested in mining frequent patterns. Unfortunately, these patterns do not offer the whole information about the correlation rate amongst the items that constitute a given pattern since they are mainly interested in appearance frequency. In this situation, many correlation measures have been proposed in order to convey information on the dependencies within sets of items. In this work, we adopt the correlation measure bond, which provides several interesting properties. Motivated by the fact that the number of correlated patterns is often huge while many of them are redundant, we propose a new exact concise representation of frequent correlated patterns associated to this measure, through the definition of a new closure operator. The proposed representation allows not only to efficiently derive the correlation rate of a given pattern but also to exactly offer its conjunctive, disjunctive and negative supports. To prove the utility of our approach, we undertake an empirical study on several benchmark data sets that are commonly used within the data mining community.

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Cited By

Bouasker SYahia SDiallo GHung CPapadopoulos G(2019)An insight into biological datamining based on rarity and correlation as constraintsProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297281(3-10)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297281
Bouasker SBen Yahia SWainwright RCorchado JBechini AHong J(2015)Key correlation mining by simultaneous monotone and anti-monotone constraints checkingProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695802(851-856)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2695802
Li AHaraguchi MOkubo Y(2012)Top-N minimization approach for indicative correlation change miningProceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition10.1007/978-3-642-31537-4_9(102-116)Online publication date: 13-Jul-2012
https://dl.acm.org/doi/10.1007/978-3-642-31537-4_9
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Information & Contributors

Information

Published In

cover image Guide Proceedings

DS'10: Proceedings of the 13th international conference on Discovery science

October 2010

382 pages

ISBN:3642161839

Editors:
Bernhard Pfahringer
The University of Waikato, Department of Computer Science, Hamilton, New Zealand
,
Geoff Holmes
The University of Waikato, Department of Computer Science, Hamilton, New Zealand
,
Achim Hoffmann
The University of New South Wales, School of Computer Science and Engineering, Sydney, Australia

Publisher

Springer-Verlag

Berlin, Heidelberg

Publication History

Published: 06 October 2010

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Cited By

Bouasker SYahia SDiallo GHung CPapadopoulos G(2019)An insight into biological datamining based on rarity and correlation as constraintsProceedings of the 34th ACM/SIGAPP Symposium on Applied Computing10.1145/3297280.3297281(3-10)Online publication date: 8-Apr-2019
https://dl.acm.org/doi/10.1145/3297280.3297281
Bouasker SBen Yahia SWainwright RCorchado JBechini AHong J(2015)Key correlation mining by simultaneous monotone and anti-monotone constraints checkingProceedings of the 30th Annual ACM Symposium on Applied Computing10.1145/2695664.2695802(851-856)Online publication date: 13-Apr-2015
https://dl.acm.org/doi/10.1145/2695664.2695802
Li AHaraguchi MOkubo Y(2012)Top-N minimization approach for indicative correlation change miningProceedings of the 8th international conference on Machine Learning and Data Mining in Pattern Recognition10.1007/978-3-642-31537-4_9(102-116)Online publication date: 13-Jul-2012
https://dl.acm.org/doi/10.1007/978-3-642-31537-4_9
Li AHaraguchi MOkubo Y(2011)Contrasting correlations by an efficient double-clique conditionProceedings of the 7th international conference on Machine learning and data mining in pattern recognition10.5555/2033831.2033871(469-483)Online publication date: 30-Aug-2011
https://dl.acm.org/doi/10.5555/2033831.2033871

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