Three Companions for Data Mining in First Order Logic

Abstract

Three companion systems, Claudien, ICL and Tilde, are presented. They use a common representation for examples and hypotheses: each example is represented by a relational database. This contrasts with the classical inductive logic programming systems such as Progol and Foil. It is argued that this representation is closer to attribute value learning and hence more natural. Furthermore, the three systems can be considered first order upgrades of typical data mining systems, which induce association rules, classification rules or decision trees respectively.