Parallel rough set based knowledge acquisition using MapReduce from big data

Junbo Zhang, Tianrui Li, Yi Pan

Abstract: Nowadays, with the volume of data growing at an unprecedented rate, big data mining and knowledge discovery have become a new challenge. Rough set theory for knowledge acquisition has been successfully applied in data mining. The recently introduced MapReduce technique has received much attention from both scientific community and industry for its applicability in big data analysis. To mine knowledge from big data, we present parallel rough set based methods for knowledge acquisition using MapReduce in this paper. Comprehensive experimental evaluation on large data sets shows that the proposed parallel methods can effectively process big data.