Interpretation of Self-Organzing Maps with Fuzzy Rules

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 a simpler, usually two-dimensional, topological structure. This mapping is able to illustrate dependencies in the data in a very intuitive manner and allows fast location of 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. Then we 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.