Comparison of similarity measures in context of rules clustering

This paper introduces five similarity measures, very well known in literature, but not because of using them to compare rules between themselves and choose the most similar one. Rules in knowledge bases are a very specific type of data representation and it is necessary to compare them carefully. The goal of the paper is to analyze the influence of using different similarity measures on the number of clusters, or the size of the representatives of the created clusters of rules. The results of the experiments are presented in Section III in order to discuss the significance of the analyzed measures and methods of rules creating.