Self-organizing maps
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
An Artificial Neural Network Model for Multi Dimension Reduction and Data Structure Exploration
SOCPAR '09 Proceedings of the 2009 International Conference of Soft Computing and Pattern Recognition
Graph based representations of density distribution and distances for self-organizing maps
IEEE Transactions on Neural Networks
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
PRSOM: a new visualization method by hybridizing multidimensional scaling and self-organizing map
IEEE Transactions on Neural Networks
Self-Organizing and Self-Evolving Neurons: A New Neural Network for Optimization
IEEE Transactions on Neural Networks
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Self-Organizing Map (SOM), being a prominent unsupervised learning algorithm, is often used for multivariate data visualization. However, SOM only preserves inter-neurons distances in the input space and not in the output space due to the rigid grid used in SOM. Visualization-induced Self-Organizing Map (ViSOM) has been proposed as a visualization-wise improved variation of the popular unsupervised SOM. However ViSOM suffers from dead neuron problem as a huge number of neurons fall outside of the data region due to the regularization effect, even when the regularization control parameter is properly chosen. In this paper, a hybrid ViSOM that employs a modified Adaptive Coordinates (AC) technique is proposed for data visualization. Empirical studies of the hybrid technique yield promising topology preserved visualizations and data structure exploration for synthetic as well as benchmarking datasets.