A Comparison of Three Strategies to Rule Induction from Data with Numerical Attributes

Our main objective was to compare two discretization techniques, both based on cluster analysis, with a new rule induction algorithm called MLEM2, in which discretization is performed simultaneously with rule induction. The MLEM2 algorithm is an extension of the existing LEM2 rule induction algorithm. The LEM2 algorithm works correctly only for symbolic attributes and is a part of the LERS data mining system. For the two strategies, based on cluster analysis, rules were induced by the LEM2 algorithm. Our results show that MLEM2 outperformed both strategies based on cluster analysis, in terms of complexity (size of rule sets) and, more importantly, error rates.