Session:

Mini-Symposia: Computational Medicine Building and Applying Intelligent Systems in Health
Tuesday, March 02, 2004, 11:30 - 11:50

Session Chair:

Peter Kokol

Paper Title:

Symbolic Approach to Unsupervised Learning

Author(s):

E. Avdicauševic, University of Maribor, Slovenia
M. Lenic, University of Maribor, Slovenia
M. Molan Stiglic, University of Maribor, Slovenia

Abstract:

As important as data mining techniques are, they still require too much technical knowledge from users. This is most evident in the process of interpretation of results, where domain experts are involved. One of the most challenging tasks in the area of knowledge discovery is to express discovered knowledge in a form, which can be understood by domain experts (e.g. medical experts). In the paper we present our approach to unsupervised learning using multivariate symbolic hybrid. Main advantage of multimethod symbolic hybrid is that learned knowledge is expressed in a form of symbolic rules. Learned knowledge is much more understandable to domain experts, which increases its value and makes it much easier to apply.