LEADER 05029nam 22008055 450 001 996465760103316 005 20200630043654.0 010 $a3-319-50137-2 024 7 $a10.1007/978-3-319-50137-6 035 $a(CKB)3710000001006511 035 $a(DE-He213)978-3-319-50137-6 035 $a(MiAaPQ)EBC6283385 035 $a(MiAaPQ)EBC5594707 035 $a(Au-PeEL)EBL5594707 035 $a(OCoLC)1076247014 035 $a(PPN)197455182 035 $a(EXLCZ)993710000001006511 100 $a20161202d2016 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aData Mining and Constraint Programming$b[electronic resource] $eFoundations of a Cross-Disciplinary Approach /$fedited by Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi 205 $a1st ed. 2016. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2016. 215 $a1 online resource (XII, 349 p. 73 illus.) 225 1 $aLecture Notes in Artificial Intelligence ;$v10101 311 $a3-319-50136-4 327 $aIntroduction 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. 330 $aA successful integration of constraint programming and data mining has the potential to lead to a new ICT paradigm with far reaching implications. It could change the face of data mining and machine learning, as well as constraint programming technology. It would not only allow one to use data mining techniques in constraint programming to identify and update constraints and optimization criteria, but also to employ constraints and criteria in data mining and machine learning in order to discover models compatible with prior knowledge. This book reports on some key results obtained on this integrated and cross- disciplinary approach within the European FP7 FET Open project no. 284715 on ?Inductive Constraint Programming? and a number of associated workshops and Dagstuhl seminars. The book is structured in five parts: background; learning to model; learning to solve; constraint programming for data mining; and showcases. . 410 0$aLecture Notes in Artificial Intelligence ;$v10101 606 $aArtificial intelligence 606 $aApplication software 606 $aComputer simulation 606 $aAlgorithms 606 $aDatabase management 606 $aData mining 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aInformation Systems Applications (incl. Internet)$3https://scigraph.springernature.com/ontologies/product-market-codes/I18040 606 $aSimulation and Modeling$3https://scigraph.springernature.com/ontologies/product-market-codes/I19000 606 $aAlgorithm Analysis and Problem Complexity$3https://scigraph.springernature.com/ontologies/product-market-codes/I16021 606 $aDatabase Management$3https://scigraph.springernature.com/ontologies/product-market-codes/I18024 606 $aData Mining and Knowledge Discovery$3https://scigraph.springernature.com/ontologies/product-market-codes/I18030 615 0$aArtificial intelligence. 615 0$aApplication software. 615 0$aComputer simulation. 615 0$aAlgorithms. 615 0$aDatabase management. 615 0$aData mining. 615 14$aArtificial Intelligence. 615 24$aInformation Systems Applications (incl. Internet). 615 24$aSimulation and Modeling. 615 24$aAlgorithm Analysis and Problem Complexity. 615 24$aDatabase Management. 615 24$aData Mining and Knowledge Discovery. 676 $a006.312 702 $aBessiere$b Christian$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aDe Raedt$b Luc$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aKotthoff$b Lars$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aNijssen$b Siegfried$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aO'Sullivan$b Barry$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aPedreschi$b Dino$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a996465760103316 996 $aData Mining and Constraint Programming$92830684 997 $aUNISA