Two Rough Set Approaches to Mining Hop Extraction Data

Abstract

Results of our research on using two approaches, both based on rough sets, to mining three data sets describing bed caking during the hop extraction process are presented. For data mining we used two methods: direct rule induction by the MLEM2 algorithm and generation of belief networks associated with conversion belief networks into rule sets by the BeliefSEEKER system. Statistics for rule sets are presented, including an error rate. Finally, six rule sets were ranked by an expert. Our results show that both our approaches to data mining are of approximately the same quality.