Data mining using synergies between self-organizing maps and inductive learning of fuzzy rules

Authors Mario Drobics
Ulrich Bodenhofer
Werner Winiwarter
Erich Peter Klement
Title Data mining using synergies between self-organizing maps and inductive learning of fuzzy rules
Booktitle Proc. Joint 9th IFSA World Congress and 20th NAFIPS Int. Conf. (IFSA-NAFIPS 2001)
Type in proceedings
Address Vancouver, BC, Canada
Mark invited
ISBN 0-7803-7079-1
Month July
Year 2001
Pages 1780-1785
SCCH ID# 114
Abstract

Identifying structures in large data sets raises a number of problems. On the one hand, many methods cannot be applied to larger data sets, while, on the other hand, the results are often hard to interpret. We address these problems by a novel three-stage approach. First, we compute a small representation of the input data using a self-organizing map. This reduces the amount of data and allows us to create two-dimensional plots of the data. Then we use this preprocessed information to identify clusters of similarity. Finally, inductive learning methods are applied to generate sets of fuzzy descriptions of these clusters. This approach is applied to three case studies, including image data and real-world data sets. The results illustrate the generality and intuitiveness of the proposed method.