The MiningZinc framework for constraint-based itemset mining

Tias Guns, Anton Dries, Guido Tack, Siegfried Nijssen, Luc De Raedt

    Research output: Chapter in Book/Report/Conference proceedingConference PaperResearch

    6 Citations (Scopus)


    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. 
    Original languageEnglish
    Title of host publicationProceedings of the 2013 IEEE International Conference on Data Mining Workshops (ICDMW 2013)
    Subtitle of host publication7-10 December 2013, Dallas, Texas
    EditorsWei Ding, Takashi Washio, Hui Xiong, George Karypis, Bhavani Thuraisingham, Diane Cook, Xindong Wu
    Place of PublicationPiscataway, NJ
    PublisherIEEE, Institute of Electrical and Electronics Engineers
    Number of pages4
    ISBN (Print)9780768551098
    Publication statusPublished - 2013
    EventIEEE International Conference on Data Mining Workshops 2013 - Dallas, United States of America
    Duration: 7 Dec 201310 Dec 2013
    Conference number: 13th


    ConferenceIEEE International Conference on Data Mining Workshops 2013
    Abbreviated titleICDMW 2013
    Country/TerritoryUnited States of America


    • Constraint programming
    • Constraint-based mining
    • Framework

    Cite this