article

Soft constraints for pattern mining

Authors:

Patrice Boizumault,

Bruno Crémilleux,

Alban LepailleurAuthors Info & Claims

Journal of Intelligent Information Systems, Volume 44, Issue 2

Pages 193 - 221

https://doi.org/10.1007/s10844-013-0281-4

Published: 01 April 2015 Publication History

Abstract

Constraint-based pattern discovery is at the core of numerous data mining tasks. Patterns are extracted with respect to a given set of constraints (frequency, closedness, size, etc). In practice, many constraints require threshold values whose choice is often arbitrary. This difficulty is even harder when several thresholds are required and have to be combined. Moreover, patterns barely missing a threshold will not be extracted even if they may be relevant. The paper advocates the introduction of softness into the pattern discovery process. By using Constraint Programming, we propose efficient methods to relax threshold constraints as well as constraints involved in patterns such as the top-k patterns and the skypatterns. We show the relevance and the efficiency of our approach through a case study in chemoinformatics for discovering toxicophores.

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Cited By

Alfaro-Paredes EAlfaro-Carrasco LUgarte W(2021)Query by Humming for Song Identification Using Voice IsolationAdvances and Trends in Artificial Intelligence. From Theory to Practice10.1007/978-3-030-79463-7_27(323-334)Online publication date: 26-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-79463-7_27

Index Terms

Soft constraints for pattern mining
1. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image Journal of Intelligent Information Systems

Journal of Intelligent Information Systems Volume 44, Issue 2

April 2015

115 pages

ISSN:0925-9902

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Copyright © Copyright © 2015 Springer Science+Business Media New York.

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Kluwer Academic Publishers

United States

Publication History

Published: 01 April 2015

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Cited By

Alfaro-Paredes EAlfaro-Carrasco LUgarte W(2021)Query by Humming for Song Identification Using Voice IsolationAdvances and Trends in Artificial Intelligence. From Theory to Practice10.1007/978-3-030-79463-7_27(323-334)Online publication date: 26-Jul-2021
https://dl.acm.org/doi/10.1007/978-3-030-79463-7_27

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