Data Mining and Knowledge Discovery in Medical Applications Using Self-Organizing Maps

Authors:
Thomas Villmann;Wieland Hermann;Michael Geyer
Affiliations:
-;-;-
Venue:
ISMDA '00 Proceedings of the First International Symposium on Medical Data Analysis
Year:
2000

Citing 7
Cited 0

On the stationary state of Kohonen's self-organizing sensory mapping

Biological Cybernetics
Self-organizing maps

Self-organizing maps
Neural maps and topographic vector quantization

Neural Networks
How to generate ordered maps by maximizing the mutual information between input and output signals

Neural Computation
Controlling the magnification factor of self-organizing feature maps

Neural Computation
Growing a hypercubical output space in a self-organizing feature map

IEEE Transactions on Neural Networks
Topology preservation in self-organizing feature maps: exact definition and measurement

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

In the contribution the authors discuss the application of Self-Organizing Maps (SOMs) for data mining and knowledge discovery in medicine. Thereby, the usually assumed but not verified topology preservation is in the main focus. Extensions of the usual SOM are offered to obtain correct results. The authots give examples for applications such as visualization and clustering.