Data Mining and Constraint Programming Foundations of a Cross-Disciplinary Approach Lecture Notes in Artificial Intelligence Series
Coordonnateurs : Bessiere Christian, De Raedt Luc, Kotthoff Lars, Nijssen Siegfried, O'Sullivan Barry, Pedreschi Dino
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.
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
Date de parution : 12-2016
Ouvrage de 349 p.
15.5x23.5 cm
Disponible chez l'éditeur (délai d'approvisionnement : 15 jours).
Prix indicatif 52,74 €
Ajouter au panierThèmes de Data Mining and Constraint Programming :
Mots-clés :
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