ENGLISH

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

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

Book information

Publisher
Springer International Publishing
Year
2016
ISBN
978-3-319-50136-9, 978-3-319-50137-6
DOI
10.1007/978-3-319-50137-6
Language
english
Format
PDF
Filesize
12 MB (13004205 bytes)
Series
Lecture Notes in Computer Science 10101
Edition
1
Pages
XII, 349\352
Time added
2016-12-28 02:00:00
Artificial Intelligence (incl. Robotics);Informati

Description

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.

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