Paper Info
Title
The MiningZinc Framework for Constraint-Based Itemset Mining
Abstract
We present Mining Zinc, a novel system for constraint-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, Mining Zinc advances the state-of-the-art of declarative data mining significantly. Key components of the Mining Zinc system are (1) a high-level and natural language for formalizing constraint-based item set mining problems in models, and (2) an infrastructure for executing these models, which supports both specialized 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, and the use of different solution methods.
Year
DOI
Venue
2013
10.1109/ICDMW.2013.38
Data Mining Workshops
Keywords
Field
DocType
data mining,constraint-based item set mining,mining zinc,miningzinc framework,declarative data,declarative approach,database system,mining zinc advance,mining zinc system,constraint-based pattern mining,specialized mining algorithm,constraint-based itemset mining,framework,constraint programming,artificial intelligence,natural language processing,high level languages
Constraint satisfaction,Fifth-generation programming language,Data mining,Data stream mining,Concept mining,Computer science,Inductive programming,Constraint programming,High-level programming language,Declarative programming
Conference
ISSN
ISBN
Citations 
2375-9232
978-1-4799-3143-9
1
PageRank 
References 
Authors
0.34
7
5
Name
Order
Citations
PageRank
Tias Guns133223.14
Anton Dries212610.83
Guido Tack337727.56
Siegfried Nijssen4110359.13
Luc De Raedt55481505.49