DocumentCode
3439733
Title
The MiningZinc Framework for Constraint-Based Itemset Mining
Author
Guns, Tias ; Dries, Anton ; Tack, Guido ; Nijssen, Siegfried ; De Raedt, Luc
Author_Institution
Dept. of Comput. Sci., KU Leuven, Leuven, Belgium
fYear
2013
fDate
7-10 Dec. 2013
Firstpage
1081
Lastpage
1084
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.
Keywords
artificial intelligence; data mining; high level languages; natural language processing; MiningZinc framework; artificial intelligence; constraint based itemset mining; constraint based pattern mining; database systems; declarative data mining; declarative programming; high-level language; natural language; Algorithm design and analysis; Data mining; Data models; Itemsets; Libraries; Motion pictures; Reactive power; constraint programming; constraint-based mining; framework;
fLanguage
English
Publisher
ieee
Conference_Titel
Data Mining Workshops (ICDMW), 2013 IEEE 13th International Conference on
Conference_Location
Dallas, TX
Print_ISBN
978-1-4799-3143-9
Type
conf
DOI
10.1109/ICDMW.2013.38
Filename
6754042
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