Abstract
In this paper, we present the newest version of the MLEM2 algorithm for rule induction, a basic component of the LERS data mining system. This version of the MLEM2 algorithm is based on local lower and upper approximations, and in its current formis presented in this paper for the first time. Additionally, we present results of experiments comparing the local version of the MLEM2 algorithm for rule induction with an older version of MLEM2, which was based on global lower and upper approximations. Our experiments show that the local version of MLEM2 is significantly better than the global version of MLEM2 (2% significance level, two-tailed Wilcoxon test).
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