A parallel method for computing rough set approximations

Junbo Zhang, Tianrui Li, Da Ruan, Zizhe Gao, Chengbing Zhao

    Research outputpeer-review


    Massive data mining and knowledge discovery present a tremendous challenge with the data volume growing at an unprecedented rate. Rough set theory has been successfully applied in data mining. The lower and upper approximations are basic concepts in rough set theory. The effective computation of approximations is vital for improving the performance of data mining or other related tasks. The recently introduced MapReduce technique has gained a lot of attention from the scientific community for its applicability in massive data analysis. This paper proposes a parallel method for computing rough set approximations. Consequently, algorithms corresponding to the parallel method based on the MapReduce technique are put forward to deal with the massive data. An extensive experimental evaluation on different large data sets shows that the proposed parallel method is effective for data mining.

    Original languageEnglish
    Pages (from-to)209-223
    Number of pages15
    JournalInformation Sciences
    StatePublished - 1 Jul 2012

    ASJC Scopus subject areas

    • Software
    • Control and Systems Engineering
    • Theoretical Computer Science
    • Computer Science Applications
    • Information Systems and Management
    • Artificial Intelligence

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