Association Learning in SOMs for Fuzzy-Classification

We present a general framework for association learning in self-organizing maps (SOMs), which can be specified for the utilization for supervised fuzzy classification. In this way, we obtain a prototype based fuzzy classification model (FLSOM), which can be easily interpreted and visualized due to the fundamental properties of SOMs. Moreover, the provided extension gives the ability to detect class similarities. We apply this approch to classification and class similarity detection for mass spectrometric data in case of cancer disease and obtain comparable results. We demonstrate that the FLSOM-based class similarity detection leads to clinically expected class similarities. Finally, this approach can be taken a semi-supervised learning approach in a twofold sense: association learning is influenced by two terms an unsupervised and a supervised learning term. Further, if no association is given for a data point, only the unsupervised learning amount is applied.