GTM: the generative topographic mapping
Neural Computation
Self-Organizing Maps
The topographic product of experts
ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I
Local vs global interactions in clustering algorithms: Advances over K-means
International Journal of Knowledge-based and Intelligent Engineering Systems
Clustering with alternative similarity functions
AIKED'08 Proceedings of the 7th WSEAS International Conference on Artificial intelligence, knowledge engineering and data bases
A novel construction of connectivity graphs for clustering and visualization
WSEAS Transactions on Computers
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We review two versions of a new topology preserving mapping, the HaToM. This mapping has previously been investigated as a data visualization tool but, in this paper, we investigate empirically the quantization errors in both versions of the mapping. We show that the more model driven version does not minimise the quantization error either when it is calculated in the usual manner or when we use the Harmonic average to do so. Somewhat surprisingly the model driven method lowers the quantization error more quickly than the data-driven method.