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Data Mining and Constraint Programming: Foundations of a Cross-Disciplinary Approach

✍ Scribed by Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi (eds.)

Publisher
Springer International Publishing
Year
2016
Tongue
English
Leaves
352
Series
Lecture Notes in Computer Science 10101
Edition
1
Category
Library
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✦ Synopsis

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 β€œInductive 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.

✦ Table of Contents

Front Matter....Pages I-XII
Front Matter....Pages 1-1
Introduction to Combinatorial Optimisation in Numberjack....Pages 3-24
Data Mining and Constraints: An Overview....Pages 25-48
Front Matter....Pages 49-49
New Approaches to Constraint Acquisition....Pages 51-76
ModelSeeker: Extracting Global Constraint Models from Positive Examples....Pages 77-95
Learning Constraint Satisfaction Problems: An ILP Perspective....Pages 96-112
Learning Modulo Theories....Pages 113-146
Front Matter....Pages 147-147
Algorithm Selection for Combinatorial Search Problems: A Survey....Pages 149-190
Advanced Portfolio Techniques....Pages 191-225
Adapting Consistency in Constraint Solving....Pages 226-253
Front Matter....Pages 255-255
Modeling in MiningZinc....Pages 257-281
Partition-Based Clustering Using Constraint Optimization....Pages 282-299
Front Matter....Pages 301-301
The Inductive Constraint Programming Loop....Pages 303-309
ICON Loop Carpooling Show Case....Pages 310-324
ICON Loop Health Show Case....Pages 325-333
ICON Loop Energy Show Case....Pages 334-347
Back Matter....Pages 349-349

✦ Subjects

Artificial Intelligence (incl. Robotics);Information Systems Applications (incl. Internet);Simulation and Modeling;Algorithm Analysis and Problem Complexity;Database Management;Data Mining and Knowledge Discovery

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