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
Automatic Labeling of Self-Organizing Maps: Making a Treasure-Map Reveal Its Secrets
PAKDD '99 Proceedings of the Third Pacific-Asia Conference on Methodologies for Knowledge Discovery and Data Mining
The Evolving Tree—A Novel Self-Organizing Network for Data Analysis
Neural Processing Letters
Advanced visualization of self-organizing maps with vector fields
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Similarity-Based Clustering
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
Visualising class distribution on self-organising maps
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Graph based representations of density distribution and distances for self-organizing maps
IEEE Transactions on Neural Networks
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
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
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The Self-Organizing Map is a popular neural network model for data analysis, for which a wide variety of visualization techniques exists. We present a novel technique that takes the density of the data into account. Our method defines graphs resulting from nearest neighbor- and radius-based distance calculations in data space and shows projections of these graph structures on the map. It can then be observed how relations between the data are preserved by the projection, yielding interesting insights into the topology of the mapping, and helping to identify outliers as well as dense regions