Interpretation of self-organizing maps with fuzzy rules
|Title||Interpretation of self-organizing maps with fuzzy rules|
|Booktitle||Proc. 12th IEEE Int.l Conf. on Tools with Artificial Intelligence (ICTAI 2000)|
|Address||Vancouver, BC, Canada|
Exploration of large and high-dimensional data sets is one of the main problems in data analysis. Self-organizing maps (SOMs) can be used to map large data sets to simpler, usually two-dimensional,topological structure. This mapping is able to illustrate dependenciesin the data in a very intuitive manner and allows fast location clusters. However, because of the black-box design of neural networks, it is difficult to get qualitative descriptions of the data. In our approach, we identify regions of interest in SOMs by using unsupervised clustering methods. Thenwe apply inductive learning methods to find fuzzy descriptions of these clusters. Through the combination of these methods, it is possible to use supervised machine learning methods to find simple and accurate linguistic descriptions of previously unknown clusters in the data.