Jumping emerging patterns with negation in transaction databases – Classification and discovery

Abstract

This paper examines jumping emerging patterns with negation (JEPNs), i.e. JEPs that can contain negated items. We analyze the basic relations between these patterns and classical JEPs in transaction databases and local reducts from the rough set theory. JEPNs provide an interesting type of knowledge and can be successfully used for classification purposes. By analogy to JEP-Classifier, we consider negJEP-Classifier and JEPN-Classifier and compare their accuracy. The results are contrasted with changes in rule set complexity. In connection with the problem of JEPN discovery, JEP-Producer and rough set methods are examined.