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020 _a9783319501376
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024 7 _a10.1007/978-3-319-50137-6
_2doi
050 4 _aQ334-342
050 4 _aTA347.A78
072 7 _aUYQ
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072 7 _aCOM004000
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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.
250 _a1st ed. 2016.
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 Artificial Intelligence,
_x2945-9141 ;
_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 _aArtificial intelligence.
_93407
650 0 _aApplication software.
_9177118
650 0 _aComputer simulation.
_95106
650 0 _aAlgorithms.
_93390
650 0 _aDatabase management.
_93157
650 0 _aData mining.
_93907
650 1 4 _aArtificial Intelligence.
_93407
650 2 4 _aComputer and Information Systems Applications.
_9177119
650 2 4 _aComputer Modelling.
_9177120
650 2 4 _aAlgorithms.
_93390
650 2 4 _aDatabase Management.
_93157
650 2 4 _aData Mining and Knowledge Discovery.
_9177121
700 1 _aBessiere, Christian.
_eeditor.
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700 1 _aDe Raedt, Luc.
_eeditor.
_4edt
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_9177123
700 1 _aKotthoff, Lars.
_eeditor.
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_9177124
700 1 _aNijssen, Siegfried.
_eeditor.
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_9177125
700 1 _aO'Sullivan, Barry.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9177126
700 1 _aPedreschi, Dino.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
_9177127
710 2 _aSpringerLink (Online service)
_9177128
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783319501369
776 0 8 _iPrinted edition:
_z9783319501383
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v10101
_9177129
856 4 0 _uhttps://doi.org/10.1007/978-3-319-50137-6
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