Affiliations: [a] Sobolev Institute of Mathematics SB RAS, Acad. Koptyug prospect 4, Novosibirsk, 630090, Russia | [b] Computer Science Department, Central Washington University, Ellensburg, WA 98926-7520, USA | [x] Institute of Mathematics, Russian Academy of Science, Novosibirsk, 630090, Russia | [y] School of Computing and Mathematical Sciences, University of Greenwich, London SE10 9LS, UK
Abstract: Knowledge discovery and data mining methods have been successful in many domains. However, their abilities to build or discover a domain theory remain unclear. This is largely due to the fact that many fundamental KDD&DM methodological questions are still unexplored such as (1) the nature of the information contained in input data relative to the domain theory, and (2) the nature of the knowledge that these methods discover. The goal of this paper is to clarify methodological questions of KDD&DM methods. This is done by using the concept of Relational Data Mining (RDM), representative measurement theory, an ontology of a subject domain, a many-sorted empirical system (algebraic structure in the first-order logic), and an ontology of a KDD&DM method. The paper concludes with a review of our RDM approach and 'Discovery' system built on this methodology that can analyze any hypotheses represented in the first-order logic and use any input by representing it in many-sorted empirical system.
Keywords: Data mining, KDD, relational data mining, probabilistic reasoning, empirical theories, theories discovery, law-like rules, requirement of maximum specificity
