%0 Conference Proceedings
%T Attribute Reduction Based on MapReduce Model and Discernibility Measure
%+ Białystok University of Technology
%A Czolombitko, Michal
%A Stepaniuk, Jaroslaw
%Z Part 2: Rough Set Methods for Big Data Analytics
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 15th IFIP International Conference on Computer Information Systems and Industrial Management (CISIM)
%C Vilnius, Lithuania
%Y Khalid Saeed
%Y Władysław Homenda
%I Springer International Publishing
%3 Computer Information Systems and Industrial Management
%V LNCS-9842
%P 55-66
%8 2016-09-14
%D 2016
%R 10.1007/978-3-319-45378-1_6
%K Rough sets
%K MapReduce
%K Reducts
%K Attribute reduction
%K Core
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 8
%2 https://inria.hal.science/hal-01637503/document
%2 https://inria.hal.science/hal-01637503/file/419526_1_En_6_Chapter.pdf
%L hal-01637503
%U https://inria.hal.science/hal-01637503
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC8
%~ IFIP-CISIM
%~ IFIP-LNCS-9842