Towards improving solution dominance with incomparability conditions: a case-study using Generator Itemset Mining

Gokberk Kocak, Özgür Akgün, Ian Miguel, Tias Guns

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

Finding interesting patterns is a challenging task in data mining. Constraint based mining is a well-known approach to this, and one for which constraint programming has been shown to be a well-suited and generic framework.
Dominance programming has been proposed as an extension that can capture an
even wider class of constraint-based mining problems, by allowing to compare
relations between patterns. In this paper, in addition to specifying a dominance
relation, we introduce the ability to specify an incomparability condition. Using
these two concepts we devise a generic framework that can do a batch-wise search
that avoids checking incomparable solutions. We extend the ESSENCE language
and underlying modelling pipeline to support this. We use generator itemset mining problem as a test case and give a declarative specification for that. We also
present preliminary experimental results on this specific problem class with a CP
solver backend to show that using the incomparability condition during search
can improve the efficiency of dominance programming and reduces the need for
post-processing to filter dominated solutions.
Original languageEnglish
Title of host publicationThe 18th workshop on Constraint Modelling and Reformulation (ModRef 2019), Proceedings
Number of pages14
Publication statusPublished - 30 Sept 2019
Event25th International Conference on Principles and Practice of Constraint Programming (CP 2019) - Stamford, United States
Duration: 30 Sept 20194 Oct 2019
Conference number: 25
http://cp2019.a4cp.org

Conference

Conference25th International Conference on Principles and Practice of Constraint Programming (CP 2019)
Abbreviated titleCP 2019
Country/TerritoryUnited States
CityStamford
Period30/09/194/10/19
Internet address

Keywords

  • Constraint programming
  • Constraint modelling
  • Data mining
  • Itemset mining
  • Pattern mining
  • Dominance programming

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