Attribute Reduction Based on MapReduce Model and Discernibility Measure

Abstract : This paper discusses two important problems of data reduction. The problems are related to computing reducts and core in rough sets. The authors use the fact that the necessary information about discernibility matrices can be computed directly from data tables, in the case of this paper so called counting tables are used. The discussed problems are of high computational complexity. Hence the authors propose to use the relevant heuristics, MRCR (MapReduce Core and Reduct Generation) implemented using the MapReduce model.
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Michal Czolombitko, Jaroslaw Stepaniuk. Attribute Reduction Based on MapReduce Model and Discernibility Measure. 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM), Sep 2016, Vilnius, Lithuania. pp.55-66, ⟨10.1007/978-3-319-45378-1_6⟩. ⟨hal-01637503⟩

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