Symbolic methodology for numeric data mining

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 Discovery algorithm. This methodology handles uniformly the numerical and interval forecasting tasks as well as c...

• Type: Journal
• Year: 2008
• Where: IDA
• Authors: Boris Kovalerchuk, Evgenii Vityaev
• Keywords: Data Mining, IDA 2008, Information Technology, Neural Networks, Relational Data