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2006
Springer

Large-scale data exploration with the hierarchically growing hyperbolic SOM

8 years 12 months ago
Large-scale data exploration with the hierarchically growing hyperbolic SOM
We introduce the Hierarchically Growing Hyperbolic Self-Organizing Map (H2 SOM) featuring two extensions of the HSOM (hyperbolic SOM): (i) a hierarchically growing variant that allows for incremental training with an automated adaptation of lattice size to achieve a prescribed quantization error and (ii) an approximate best match search that utilizes the special structure of the hyperbolic lattice to achieve a tremendous speed-up for large map sizes. Using the MNIST and the Reuters-21578 database as benchmark datasets, we show that the H2 SOM yields a highly efficient visualization algorithm that combines the virtues of the SOM with extremely rapid training and low quantization and classification errors. Key words: Hyperbolic Self-organizing maps, Growing network, Hierarchical Clustering, Text Mining
Jörg Ontrup, Helge Ritter
Added 14 Dec 2010
Updated 14 Dec 2010
Type Journal
Year 2006
Where NN
Authors Jörg Ontrup, Helge Ritter
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