Document Type
Article
Department or Administrative Unit
Computer Science
Publication Date
4-16-2008
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.
Recommended Citation
Kovalerchuk, B., & Vityaev, E. (2008). Symbolic methodology for numeric data mining. Intelligent Data Analysis, 12(2), 165–188. https://doi.org/10.3233/ida-2008-12203
Journal
Intelligent Data Analysis
Rights
© 2008 – IOS Press and the authors. All rights reserved.
Comments
Please note: This is the author’s version of a work. Changes may have been made to this work since it was submitted for publication. The final publication is available at IOS Press here.