Measures of Quality of Rulesets Extracted from Data

The paper deals with quality measures of whole sets of rules extracted from data, as a counterpart to more commonly used measures of individual rules. This research has been motivated by increasingly frequent extraction of non-classification rules, such as association rules and rules of observational logic, in real-world data mining tasks. The paer sketches the typology of rules extraction methods and of their rulesets, and recalls that quality measures for whole sets of rules have been so far used only in the case of classification rulesets. It then proposes three possible ways how such measures can be extended to general rulesets. The paper also recalls the possibility to measure the dependence of classification ruleset on parameters of the classification method by means of ROC curves, and proposes a generalization of ROC curves to general rulesets. Finally, a brief illustration on rulesets extracted by means of the method GUHA is given.