Visualising Class Distribution on Self-Organising Maps

Abstract

The Self-Organising Map is a popular unsupervised neural network model which has been used successfully in various contexts for clustering data. Even though labelled data is not required for the training process, in many applications class labelling of some sort is available. A visualisation uncovering the distribution and arrangement of the classes over the map can help the user to gain a better understanding and analysis of the mapping created by the SOM, e.g. through comparing the results of the manual labelling and automatic arrangement. In this paper, we present such a visualisation technique, which smoothly colours a SOM according to the distribution and location of the given class labels. It allows the user to easier assess the quality of the manual labelling by highlighting outliers and border data close to different classes.