Self-organizing maps
GTM: the generative topographic mapping
Neural Computation
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Towards systematic design of distance functions for data mining applications
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
A Picture from a Thousand Words
Computing in Science and Engineering
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
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
Adaptive FIR neural model for centroid learning in self-organizing maps
IEEE Transactions on Neural Networks
KSEM'10 Proceedings of the 4th international conference on Knowledge science, engineering and management
Visualising clusters in self-organising maps with minimum spanning trees
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
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
Hi-index | 0.00 |
Self-Organizing Maps have been applied in various industrial applications and have proven to be a valuable data mining tool. In order to fully benefit from their potential, advanced visualization techniques assist the user in analyzing and interpreting the maps. We propose two new methods for depicting the SOM based on vector fields, namely the Gradient Field and Borderline visualization techniques, to show the clustering structure at various levels of detail. We explain how this method can be used on aggregated parts of the SOM that show which factors contribute to the clustering structure, and show how to use it for finding correlations and dependencies in the underlying data. We provide examples on several artificial and real-world data sets to point out the strengths of our technique, specifically as a means to combine different types of visualizations offering effective multidimensional information visualization of SOMs.