Abstract: The data analysis techniques of the PolyAnalyst data mining system are based on the automated synthesis of functional programs treated as the multi-dimensional non-linear regression models. This approach provides the system with two valuable properties: 1) it can discover in data the hidden relations that might be of a great variety of forms, 2) it can explore arbitrarily complexly structured data if the corresponding data access primitives are provided. The paper contains a formal description of the formal version of the basic PolyAnalyst mechanisms, which are utilized in the general case, as well as in a particular case of data organized as a set of attribute values (SAV), which is the most common format for data explored by KDD methods.
Copyright: © Springer-Verlag Berlin Heidelberg 1998
Cite this paper as: Kiselev M.V., Ananyan S.M., Arseniev S.B. (1998) PolyAnalyst data analysis technique and its specialization for processing data organized as a set of attribute values. In: Żytkow J.M., Quafafou M. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 1998. Lecture Notes in Computer Science, vol 1510. Springer, Berlin, Heidelberg