大葉大學圖書館 |

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

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
杜威分類號:	006.312
書名/作者:	Data mining and constraint programming : foundations of a cross-disciplinary approach // edited by Christian Bessiere ... [et al.].
其他作者:	Bessiere, Christian.
出版者:	Cham : : Springer International Publishing :, 2016.
面頁冊數:	xii, 349 p. : : ill., digital ;; 24 cm.
Contained By:	Springer eBooks
標題:	Data mining.
標題:	Constraint programming (Computer science)
標題:	Computer Science.
標題:	Artificial Intelligence (incl. Robotics)
標題:	Information Systems Applications (incl. Internet)
標題:	Simulation and Modeling.
標題:	Algorithm Analysis and Problem Complexity.
標題:	Database Management.
標題:	Data Mining and Knowledge Discovery.
ISBN:	9783319501376
ISBN:	9783319501369
內容註:	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.
摘要、提要註:	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 /
LDR:02674nam a2200337 a 4500 001 476994
003 DE-He213
005 20161202191739.0
006 m d
007 cr nn 008maaau
008 181208s2016 gw s 0 eng d
020 $a 9783319501376 $q (electronic bk.)
020 $a 9783319501369 $q (paper)
024 7 $a 10.1007/978-3-319-50137-6 $2 doi
035 $a 978-3-319-50137-6
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.D343
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.312 $2 23
090 $a QA76.9.D343 $b D232 2016
245 0 0 $a Data mining and constraint programming $h [electronic resource] : $b foundations of a cross-disciplinary approach / $c edited by Christian Bessiere ... [et al.].
260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2016.
300 $a xii, 349 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in computer science, $x 0302-9743 ; $v 10101
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.
650 0 $a Data mining. $3 337740
650 0 $a Constraint programming (Computer science) $3 386137
650 1 4 $a Computer Science. $3 423143
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 463642
650 2 4 $a Information Systems Applications (incl. Internet) $3 463679
650 2 4 $a Simulation and Modeling. $3 463796
650 2 4 $a Algorithm Analysis and Problem Complexity. $3 463701
650 2 4 $a Database Management. $3 463966
650 2 4 $a Data Mining and Knowledge Discovery. $3 464541
700 1 $a Bessiere, Christian. $3 688153
710 2 $a SpringerLink (Online service) $3 463450
773 0 $t Springer eBooks
830 0 $a Lecture notes in computer science ; $v 7103. $3 466200
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-50137-6
950 $a Computer Science (Springer-11645)