• 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