Topology representing networks
Neural Networks
Extensions and modifications of the Kohenen-SOM and applications in remote sensing image analysis
Self-Organizing neural networks
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
A new model of self-organizing neural networks and its application in data projection
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
A new approach for data clustering and visualization using self-organizing maps
Expert Systems with Applications: An International Journal
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The Self-Organizing Map (SOM), a powerful method for clustering and knowledge discovery, has been used effectively for remote sensing spectral images which often have high-dimensional feature vectors (spectra) and many meaningful clusters with varying statistics. However, a learned SOM needs postprocessing to identify the clusters, which is typically done interactively from various visualizations. What aspects of the SOM's knowledge are presented by a visualization has great importance for cluster capture. We present our recent scheme, CONNvis, which achieves detailed delineation of cluster boundaries by rendering data topology on the SOM lattice. We show discovery through CONNvis clustering in a remote sensing spectral image from the Mars Exploration Rover Spirit.