Topology representing networks
Neural Networks
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Exploiting data topology in visualization and clustering of self-organizing maps
IEEE Transactions on Neural Networks
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
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
Automatic Cluster Detection in Kohonen's SOM
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
Vector quantization based approximate spectral clustering of large datasets
Pattern Recognition
ICONIP'12 Proceedings of the 19th international conference on Neural Information Processing - Volume Part II
A sample-based hierarchical adaptive K-means clustering method for large-scale video retrieval
Knowledge-Based Systems
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
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The self-organizing map (SOM) is a powerful method formanifold learning because of producing a 2-D spatially ordered quantization of a higher dimensional data space on a rigid lattice and adaptively determining optimal approximation of the (unknown) density distribution of the data. However, a postprocessing visualization scheme is often required to capture the data manifold. A recent visualization scheme CONNvis, which is shown effective for clustering, uses a topology representing graph that shows detailed local data distribution within receptive fields. This brief proposes that this graph representation can be adapted to show local distances. The proposed graphs of local density and local distances provide tools to analyze the correlation between these two information and to merge them in various ways to achieve an advanced visualization. The brief also gives comparisons for several synthetic data sets.