article

Soft constraint based pattern mining

Authors:

Stefano Bistarelli,

Francesco BonchiAuthors Info & Claims

Data & Knowledge Engineering, Volume 62, Issue 1

Pages 118 - 137

https://doi.org/10.1016/j.datak.2006.07.008

Published: 01 July 2007 Publication History

Abstract

The paradigm of pattern discovery based on constraints was introduced with the aim of providing to the user a tool to drive the discovery process towards potentially interesting patterns, with the positive side effect of achieving a more efficient computation. So far the research on this paradigm has mainly focused on the latter aspect: the development of efficient algorithms for the evaluation of constraint-based mining queries. Due to the lack of research on methodological issues, the constraint-based pattern mining framework still suffers from many problems which limit its practical relevance. In this paper, we analyze such limitations and we show how they flow out from the same source: the fact that in the classical constraint-based mining, a constraint is a rigid boolean function which returns either true or false. Indeed, interestingness is not a dichotomy. Following this consideration, we introduce the new paradigm of pattern discovery based on Soft Constraints, where constraints are no longer rigid boolean functions. Albeit based on a simple idea, our proposal has many merits: it provides a rigorous theoretical framework, which is very general (having the classical paradigm as a particular instance), and which overcomes all the major methodological drawbacks of the classical constraint-based paradigm, representing an important step further towards practical pattern discovery.

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Cited By

Hosseininasab Avan Hoeve WCire A(2019)Constraint-based sequential pattern mining with decision diagramsProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33011495(1495-1502)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33011495
Nadisic NCoussat ACerf L(2019)Mining skypatterns in fuzzy tensorsData Mining and Knowledge Discovery10.1007/s10618-019-00640-433:5(1298-1322)Online publication date: 1-Sep-2019
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Soft constraint based pattern mining

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cover image Data & Knowledge Engineering

Data & Knowledge Engineering Volume 62, Issue 1

July, 2007

198 pages

ISSN:0169-023X

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Copyright © Elsevier B.V. © 2006.

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Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 July 2007

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Hosseininasab Avan Hoeve WCire A(2019)Constraint-based sequential pattern mining with decision diagramsProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33011495(1495-1502)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33011495
Nadisic NCoussat ACerf L(2019)Mining skypatterns in fuzzy tensorsData Mining and Knowledge Discovery10.1007/s10618-019-00640-433:5(1298-1322)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s10618-019-00640-4
Ugarte WBoizumault PCrmilleux BLepailleur ALoudni SPlantevit MRassi CSoulet A(2017)Skypattern miningArtificial Intelligence10.1016/j.artint.2015.04.003244:C(48-69)Online publication date: 1-Mar-2017
https://dl.acm.org/doi/10.1016/j.artint.2015.04.003
Ugarte WBoizumault PLoudni SCrémilleux BLepailleur A(2015)Soft constraints for pattern miningJournal of Intelligent Information Systems10.1007/s10844-013-0281-444:2(193-221)Online publication date: 1-Apr-2015
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Kerdprasop NKerdprasop K(2011)The discovery of frequent patterns with logic and constraint programmingProceedings of the 13th WSEAS international conference on mathematical methods, computational techniques and intelligent systems, and 10th WSEAS international conference on non-linear analysis, non-linear systems and chaos, and 7th WSEAS international conference on dynamical systems and control, and 11th WSEAS international conference on Wavelet analysis and multirate systems: recent researches in computational techniques, non-linear systems and control10.5555/2039846.2039881(198-203)Online publication date: 1-Jul-2011
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Boley MLucchese CPaurat DGärtner TApte CGhosh JSmyth P(2011)Direct local pattern sampling by efficient two-step random proceduresProceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2020408.2020500(582-590)Online publication date: 21-Aug-2011
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Domshlak CHüllermeier EKaci SPrade H(2011)Preferences in AIArtificial Intelligence10.1016/j.artint.2011.03.004175:7-8(1037-1052)Online publication date: 1-May-2011
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Bonchi FGiannotti FLucchese COrlando SPerego RTrasarti R(2006)On interactive pattern mining from relational databasesProceedings of the 5th international conference on Knowledge discovery in inductive databases10.5555/1777194.1777200(42-62)Online publication date: 18-Sep-2006
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