Topology representing networks
Neural Networks
Self-organizing maps
Extensions and modifications of the Kohenen-SOM and applications in remote sensing image analysis
Self-Organizing neural networks
Using Smoothed Data Histograms for Cluster Visualization in Self-Organizing Maps
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Adaptive double self-organizing maps for clustering gene expression profiles
Neural Networks - 2003 Special issue: Advances in neural networks research — IJCNN'03
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
Some new indexes of cluster validity
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
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
Explicit Magnification Control of Self-Organizing Maps for “Forbidden” Data
IEEE Transactions on Neural Networks
A nonlinear projection method based on Kohonen's topology preserving maps
IEEE Transactions on Neural Networks
DS '08 Proceedings of the 11th International Conference on Discovery Science
Exploring Topology Preservation of SOMs with a Graph Based Visualization
IDEAL '08 Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
Learning Highly Structured Manifolds: Harnessing the Power of SOMs
Similarity-Based Clustering
WSOM '09 Proceedings of the 7th International Workshop on Advances in Self-Organizing Maps
Multivariate Student-t self-organizing maps
Neural Networks
Graph based representations of density distribution and distances for self-organizing maps
IEEE Transactions on Neural Networks
Adaptive FIR neural model for centroid learning in self-organizing maps
IEEE Transactions on Neural Networks
Indexability, concentration, and VC theory
Proceedings of the Third International Conference on SImilarity Search and APplications
Probabilistic self-organizing maps for qualitative data
Neural Networks
Multiple view clustering using a weighted combination of exemplar-based mixture models
IEEE Transactions on Neural Networks
Spectral clustering as an automated SOM segmentation tool
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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
Computers and Electronics in Agriculture
Vector quantization based approximate spectral clustering of large datasets
Pattern Recognition
Indexability, concentration, and VC theory
Journal of Discrete Algorithms
Improvements on the visualization of clusters in geo-referenced data using Self-Organizing Maps
Computers & Geosciences
Unsupervised neural techniques applied to MR brain image segmentation
Advances in Artificial Neural Systems - Special issue on Advances in Unsupervised Learning Techniques Applied to Biosciences and Medicine
Two fully-unsupervised methods for MR brain image segmentation using SOM-based strategies
Applied Soft Computing
LVQ-SVM based CAD tool applied to structural MRI for the diagnosis of the Alzheimer's disease
Pattern Recognition Letters
Self-Organizing Hidden Markov Model Map (SOHMMM)
Neural Networks
Improving MR brain image segmentation using self-organising maps and entropy-gradient clustering
Information Sciences: an International Journal
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The self-organizing map (SOM) is a powerful method for visualization, cluster extraction, and data mining. It has been used successfully for data of high dimensionality and complexity where traditional methods may often be insufficient. In order to analyze data structure and capture cluster boundaries from the SOM, one common approach is to represent the SOM's knowledge by visualization methods. Different aspects of the information learned by the SOM are presented by existing methods, but data topology, which is present in the SOM's knowledge, is greatly underutilized. We show in this paper that data topology can be integrated into the visualization of the SOM and thereby provide a more elaborate view of the cluster structure than existing schemes. We achieve this by introducing a weighted Delaunay triangulation (a connectivity matrix) and draping it over the SOM. This new visualization, CONNvis, also shows both forward and backward topology violations along with the severity of forward ones, which indicate the quality of the SOM learning and the data complexity. CONNvis greatly assists in detailed identification of cluster boundaries. We demonstrate the capabilities on synthetic data sets and on a real 8-D remote sensing spectral image.