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001 978-3-319-50137-6
003 DE-He213
005 20200421112050.0
007 cr nn 008mamaa
008 161202s2016 gw | s |||| 0|eng d
020 _a9783319501376
_9978-3-319-50137-6
024 7 _a10.1007/978-3-319-50137-6
_2doi
050 4 _aQ334-342
050 4 _aTJ210.2-211.495
072 7 _aUYQ
_2bicssc
072 7 _aTJFM1
_2bicssc
072 7 _aCOM004000
_2bisacsh
082 0 4 _a006.3
_223
245 1 0 _aData Mining and Constraint Programming
_h[electronic resource] :
_bFoundations of a Cross-Disciplinary Approach /
_cedited by Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2016.
300 _aXII, 349 p. 73 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v10101
505 0 _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.
520 _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. .
650 0 _aComputer science.
650 0 _aAlgorithms.
650 0 _aDatabase management.
650 0 _aData mining.
650 0 _aArtificial intelligence.
650 0 _aComputer simulation.
650 1 4 _aComputer Science.
650 2 4 _aArtificial Intelligence (incl. Robotics).
650 2 4 _aInformation Systems Applications (incl. Internet).
650 2 4 _aSimulation and Modeling.
650 2 4 _aAlgorithm Analysis and Problem Complexity.
650 2 4 _aDatabase Management.
650 2 4 _aData Mining and Knowledge Discovery.
700 1 _aBessiere, Christian.
_eeditor.
700 1 _aDe Raedt, Luc.
_eeditor.
700 1 _aKotthoff, Lars.
_eeditor.
700 1 _aNijssen, Siegfried.
_eeditor.
700 1 _aO'Sullivan, Barry.
_eeditor.
700 1 _aPedreschi, Dino.
_eeditor.
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319501369
830 0 _aLecture Notes in Computer Science,
_x0302-9743 ;
_v10101
856 4 0 _uhttp://dx.doi.org/10.1007/978-3-319-50137-6
912 _aZDB-2-SCS
912 _aZDB-2-LNC
942 _cEBK
999 _c57168
_d57168