Data Mining and Constraint Programming

Foundations of a Cross-Disciplinary Approach

  • Christian Bessiere
  • Luc De Raedt
  • Lars Kotthoff
  • Siegfried Nijssen
  • Barry O'Sullivan
  • Dino Pedreschi

Part of the Lecture Notes in Computer Science book series (LNCS, volume 10101)

Also part of the Lecture Notes in Artificial Intelligence book sub series (LNAI, volume 10101)

Table of contents

  1. Front Matter
    Pages I-XII
  2. Background

    1. Front Matter
      Pages 1-1
    2. Barry Hurley, Barry O’Sullivan
      Pages 3-24
    3. Valerio Grossi, Dino Pedreschi, Franco Turini
      Pages 25-48
  3. Learning to Model

    1. Front Matter
      Pages 49-49
    2. Christian Bessiere, Abderrazak Daoudi, Emmanuel Hebrard, George Katsirelos, Nadjib Lazaar, Younes Mechqrane et al.
      Pages 51-76
    3. Nicolas Beldiceanu, Helmut Simonis
      Pages 77-95
    4. Luc De Raedt, Anton Dries, Tias Guns, Christian Bessiere
      Pages 96-112
    5. Andrea Passerini
      Pages 113-146
  4. Learning to Solve

    1. Front Matter
      Pages 147-147
    2. Barry Hurley, Lars Kotthoff, Yuri Malitsky, Deepak Mehta, Barry O’Sullivan
      Pages 191-225
    3. Amine Balafrej, Christian Bessiere, Anastasia Paparrizou, Gilles Trombettoni
      Pages 226-253
  5. Constraint Programming for Data Mining

    1. Front Matter
      Pages 255-255
    2. Anton Dries, Tias Guns, Siegfried Nijssen, Behrouz Babaki, Thanh Le Van, Benjamin Negrevergne et al.
      Pages 257-281
    3. Valerio Grossi, Tias Guns, Anna Monreale, Mirco Nanni, Siegfried Nijssen
      Pages 282-299
  6. Showcases

    1. Front Matter
      Pages 301-301
    2. Christian Bessiere, Luc De Raedt, Tias Guns, Lars Kotthoff, Mirco Nanni, Siegfried Nijssen et al.
      Pages 303-309
    3. Mirco Nanni, Lars Kotthoff, Riccardo Guidotti, Barry O’Sullivan, Dino Pedreschi
      Pages 310-324
    4. Barry Hurley, Lars Kotthoff, Barry O’Sullivan, Helmut Simonis
      Pages 325-333

About this book

Introduction

A successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge.

This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on “Inductive Constraint Programming” and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases. 

Keywords

combinatorial optimization constraint optimization constraint solving inductive logic programming machine learning algorithm selection combinatorial search constraint programming constraint satisfaction data mining finite domain constraint models hybrid domains model acquisition partition-based clustering planning quality of service resource optimization resource-allocation scheduling state-of-the-art solvers

Editors and affiliations

  • Christian Bessiere
    • 1
  • Luc De Raedt
    • 2
  • Lars Kotthoff
    • 3
  • Siegfried Nijssen
    • 4
  • Barry O'Sullivan
    • 5
  • Dino Pedreschi
    • 6
  1. 1.Université Montpellier 2MontpellierFrance
  2. 2.KU LeuvenHeverleeBelgium
  3. 3.University of British ColumbiaVancouverCanada
  4. 4.Université Catholique de LouvainLouvain-la-NeuveBelgium
  5. 5.University College CorkCorkIreland
  6. 6.University of PisaPisaItaly

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-319-50137-6
  • Copyright Information Springer International Publishing AG 2016
  • Publisher Name Springer, Cham
  • eBook Packages Computer Science
  • Print ISBN 978-3-319-50136-9
  • Online ISBN 978-3-319-50137-6
  • Series Print ISSN 0302-9743
  • Series Online ISSN 1611-3349
  • About this book