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Data mining and constraint programmi...

Bessiere, Christian.

Data mining and constraint programming[electronic resource] :foundations of a cross-disciplinary approach /

レコード種別:	言語・文字資料 (印刷物) : 単行資料
[NT 15000414] null:	006.312
タイトル / 著者:	Data mining and constraint programming : foundations of a cross-disciplinary approach // edited by Christian Bessiere ... [et al.].
その他の著者:	Bessiere, Christian.
出版された:	Cham : : Springer International Publishing :, 2016.
記述:	xii, 349 p. : : ill., digital ;; 24 cm.
含まれています:	Springer eBooks
主題:	Data mining.
主題:	Constraint programming (Computer science)
主題:	Computer Science.
主題:	Artificial Intelligence (incl. Robotics)
主題:	Information Systems Applications (incl. Internet)
主題:	Simulation and Modeling.
主題:	Algorithm Analysis and Problem Complexity.
主題:	Database Management.
主題:	Data Mining and Knowledge Discovery.
国際標準図書番号 (ISBN) :	9783319501376
国際標準図書番号 (ISBN) :	9783319501369
[NT 15000228] null:	Introduction to Combinatorial Optimisation in Numberjack -- Data Mining and Constraints: An Overview -- New Approaches to Constraint Acquisition -- ModelSeeker: Extracting Global Constraint Models from Positive Examples -- Learning Constraint Satisfaction Problems: An ILP Perspective -- Learning Modulo Theories -- Algorithm Selection for Combinatorial Search Problems: A Survey -- Adapting Consistency in Constraint Solving -- Modeling in MiningZinc -- Partition-Based Clustering Using Constraint Optimisation -- The Inductive Constraint Programming Loop -- ICON Loop Carpooling Show Case -- ICON Loop Health Show Case -- ICON Loop Energy Show Case.
[NT 15000229] null:	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.
電子資源:	http://dx.doi.org/10.1007/978-3-319-50137-6

Data mining and constraint programming[electronic resource] :foundations of a cross-disciplinary approach /
Data mining and constraint programmingfoundations of a cross-disciplinary approach /[electronic resource] :edited by Christian Bessiere ... [et al.]. - Cham :Springer International Publishing :2016. - xii, 349 p. :ill., digital ;24 cm. - Lecture notes in computer science,101010302-9743 ;. - Lecture notes in computer science ;7103..

Introduction to Combinatorial Optimisation in Numberjack -- Data Mining and Constraints: An Overview -- New Approaches to Constraint Acquisition -- ModelSeeker: Extracting Global Constraint Models from Positive Examples -- Learning Constraint Satisfaction Problems: An ILP Perspective -- Learning Modulo Theories -- Algorithm Selection for Combinatorial Search Problems: A Survey -- Adapting Consistency in Constraint Solving -- Modeling in MiningZinc -- Partition-Based Clustering Using Constraint Optimisation -- The Inductive Constraint Programming Loop -- ICON Loop Carpooling Show Case -- ICON Loop Health Show Case -- ICON Loop Energy Show Case.

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.

ISBN: 9783319501376

Standard No.: 10.1007/978-3-319-50137-6doiSubjects--Topical Terms:

337740
Data mining.

LC Class. No.: QA76.9.D343

Dewey Class. No.: 006.312

Data mining and constraint programming[electronic resource] :foundations of a cross-disciplinary approach /
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505 0 $a Introduction to Combinatorial Optimisation in Numberjack -- Data Mining and Constraints: An Overview -- New Approaches to Constraint Acquisition -- ModelSeeker: Extracting Global Constraint Models from Positive Examples -- Learning Constraint Satisfaction Problems: An ILP Perspective -- Learning Modulo Theories -- Algorithm Selection for Combinatorial Search Problems: A Survey -- Adapting Consistency in Constraint Solving -- Modeling in MiningZinc -- Partition-Based Clustering Using Constraint Optimisation -- The Inductive Constraint Programming Loop -- ICON Loop Carpooling Show Case -- ICON Loop Health Show Case -- ICON Loop Energy Show Case.
520 $a 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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http://dx.doi.org/10.1007/978-3-319-50137-6

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