tables with many-valued decisions. We construct decision rules directly for rows of decision
table, based on paths in decision tree, and based on attributes contained in a test (super-
reduct). Experimental results for the data sets taken from UCI Machine Learning Repository,
contain comparison of the maximum and the average length of rules for the mentioned
approaches.