Affiliations: [a] Central Washington University, Ellensburg, WA 98926-7520, USA | [b] Institute of Mathematics, Russian Academy of Science, Novosibirsk, 630090, Russia | [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
Correspondence:
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Corresponding author: Boris Kovalerchuk, Central Washington University, Ellensburg, WA 98926-7520, USA. Tel.: +1 509 963 1438; Fax: +1 509 963 1449; E-mail: [email protected].
Abstract: Currently statistical and artificial neural network methods dominate in data mining applications. Alternative relational (symbolic) data mining methods have shown their effectiveness in robotics, drug design, and other areas. Neural networks and decision tree methods have serious limitations in capturing relations that may have a variety of forms. Learning systems based on symbolic first-order logic (FOL) representations capture relations naturally. The learned regularities are understandable directly in domain terms that help to build a domain theory. This paper describes relational data mining methodology and develops it further for numeric data such as financial and spatial data. This includes (1) comparing the attribute-value representation with the relational representation, (2) defining a new concept of joint relational representations, (3) a process of their use, and the Discovery algorithm. This methodology handles uniformly the numerical and interval forecasting tasks as well as classification tasks. It is shown that Relational Data Mining (RDM) can handle multiple constrains, initial rules and background knowledge very naturally to reduce the search space in contrast with attribute-based data mining. Theoretical concepts are illustrated with examples from financial and image processing domains.
Keywords: Data mining, KDD, relational data mining, numerical data, first order logic, probabilistic first order logic rules, stock market, image processing, edge detection
