Go to main content

Data Mining and Constraint Programming : Foundations of a Cross-Disciplinary Approach / edited by Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi.

Bessiere, Christian, editor.; De Raedt, Luc, editor.; Kotthoff, Lars, editor.; Nijssen, Siegfried, editor.; O'Sullivan, Barry, editor.; Pedreschi, Dino, editor.
2016
Q334-342
Available Online
Formats
Format
BibTeX
MARCXML
TextMARC
MARC
DublinCore
EndNote
NLM
RefWorks
RIS
Add to Basket
Cite
Citation

Linked e-resources

Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks

Details

Title
Data Mining and Constraint Programming : Foundations of a Cross-Disciplinary Approach / edited by Christian Bessiere, Luc De Raedt, Lars Kotthoff, Siegfried Nijssen, Barry O'Sullivan, Dino Pedreschi.
ISBN
9783319501376
3319501372
9783319501369
DOI
https://doi.org/10.1007/978-3-319-50137-6
Published
Cham : Springer International Publishing : Imprint : Springer, 2016.
Language
English
Description
1 online resource (xii, 349 pages) : illustrations.
Item Number
10.1007/978-3-319-50137-6 doi
Call Number
Q334-342
Dewey Decimal Classification
006.3
Summary
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.
Access Note
Access limited to authorized users.
Digital File Characteristics
text file PDF
Added Author
Bessiere, Christian, editor.
De Raedt, Luc, editor.
Kotthoff, Lars, editor.
Nijssen, Siegfried, editor.
O'Sullivan, Barry, editor.
Pedreschi, Dino, editor.
Series
Lecture notes in computer science ; 10101.
Available in Other Form
Print version: 9783319501369
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
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.

Browse Subjects

Show more subjects...

Statistics

from
to
Export