Three Strategies to Rule Induction from Data with Numerical Attributes

Abstract

Rule induction from data with numerical attributes must be accompanied by discretization. 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, working correctly only for symbolic attributes and being 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 and LEM2, in terms of complexity (size of rule sets and the total number of conditions) and, more importantly, in terms of error rates.