Topology representing networks
Neural Networks
Self-organizing maps
Dynamic topology representing networks
Neural Networks
Local multidimensional scaling
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Online data visualization using the neural gas network
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Topology representing network map: a new tool for visualization of high-dimensional data
Transactions on computational science I
Nonlinear projection using geodesic distances and the neural gas network
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part I
ViSOM - a novel method for multivariate data projection and structure visualization
IEEE Transactions on Neural Networks
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As data analysis tasks often have to face the analysis of huge and complex data sets there is a need for new algorithms that combine vector quantization and mapping methods to visualize the hidden data structure in a low-dimensional vector space. In this paper a new class of algorithms is defined. Topology representing networks are applied to quantify and disclose the data structure and different nonlinear mapping algorithms for the low-dimensional visualization are applied for the mapping of the quantized data. To evaluate the main properties of the resulted topology representing network based mapping methods a detailed analysis based on the wine benchmark example is given.