A parallel method for computing rough set approximations

  • Authors:

  • Junbo Zhang;Tianrui Li;Da Ruan;Zizhe Gao;Chengbing Zhao

  • Affiliations:

  • School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;Belgian Nuclear Research Centre (SCKCEN), Boeretang 200, 2400 Mol, Belgium and Department of Applied Mathematics and Computer Science, Ghent University, 9000 Gent, Belgium;School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China;School of Information Science and Technology, Southwest Jiaotong University, Chengdu 610031, China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2012

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Abstract

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.