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雷达卡
Foundations of a Cross-Disciplinary Approach
Editors: Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi
Reports on key results obtained in the field of data mining and constraint programming
Integrated and cross-disciplinary approach
Features state-of-the art research
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.
Table of contents
Front Matter
Pages I-XII
Background
Front Matter
Pages 1-1
Introduction to Combinatorial Optimisation in Numberjack
Pages 3-24
Data Mining and Constraints: An Overview
Pages 25-48
Learning to Model
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
Learning to Solve
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
Constraint Programming for Data Mining
Front Matter
Pages 255-255
Modeling in MiningZinc
Pages 257-281
Partition-Based Clustering Using Constraint Optimization
Pages 282-299
Showcases
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
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