Overview
- 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
- Includes supplementary material: sn.pub/extras
Part of the book series: Lecture Notes in Computer Science (LNCS, volume 10101)
Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)
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Table of contents (15 chapters)
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Front Matter
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Background
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Front Matter
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Constraint Programming for Data Mining
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Front Matter
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Showcases
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Front Matter
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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
About this book
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.
Editors and Affiliations
Bibliographic Information
Book Title: Data Mining and Constraint Programming
Book Subtitle: Foundations of a Cross-Disciplinary Approach
Editors: Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi
Series Title: Lecture Notes in Computer Science
DOI: https://doi.org/10.1007/978-3-319-50137-6
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2016
Softcover ISBN: 978-3-319-50136-9Published: 06 December 2016
eBook ISBN: 978-3-319-50137-6Published: 01 December 2016
Series ISSN: 0302-9743
Series E-ISSN: 1611-3349
Edition Number: 1
Number of Pages: XII, 349
Number of Illustrations: 73 b/w illustrations
Topics: Artificial Intelligence, Information Systems Applications (incl. Internet), Simulation and Modeling, Algorithm Analysis and Problem Complexity, Database Management, Data Mining and Knowledge Discovery