Title: The MiningZinc framework for constraint-based itemset mining
Authors: Guns, Tias
Dries, Anton
Tack, Guido
Nijssen, Siegfried
De Raedt, Luc
Issue Date: Dec-2013
Publisher: IEEE Computer Society
Host Document: 13th IEEE International Conference on Data Mining Workshops pages:1081-1084
Conference: Demo track at the IEEE International Conference on Data Mining edition:13 location:Dallas, Texas, USA date:7-10 December 2013
Abstract: We present MiningZinc, a novel system for con-
straint-based pattern mining. It provides a declarative approach
to data mining, where a user specifies a problem in terms
of constraints and the system employs advanced techniques to
efficiently find solutions. Declarative programming and modeling
are common in artificial intelligence and in database systems,
but not so much in data mining; by building on ideas from
these communities, MiningZinc advances the state-of-the-art of
declarative data mining significantly. Key components of the
MiningZinc system are (1) a high-level and natural language for
formalizing constraint-based itemset mining problems in models,
and (2) an infrastructure for executing these models, which
supports both specialised mining algorithms as well as generic
constraint solving systems. A use case demonstrates the generality
of the language, as well as its flexibility towards adding and
modifying constraints and data, as well as the use of different
solution methods.
ISBN: 978-0-7695-5109-8
ISSN: 1550-4786
Publication status: published
KU Leuven publication type: IC
Appears in Collections:Informatics Section

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