Voronoi diagrams—a survey of a fundamental geometric data structure
ACM Computing Surveys (CSUR)
Self-Organizing Maps
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A SOM Based Cluster Visualization and Its Application for False Coloring
IJCNN '00 Proceedings of the IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3 - Volume 3
Advanced visualization techniques for self-organizing maps with graph-based methods
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
A novel technique for data visualization based on SOM
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Creating ambient music spaces in real and virtual worlds
Multimedia Tools and Applications
A discussion on visual interactive data exploration using self-organizing maps
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
On wires and cables: content analysis of wikileaks using self-organising maps
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Analysing the similarity of album art with self-organising maps
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
Exploiting the self-organizing financial stability map
Engineering Applications of Artificial Intelligence
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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.