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- E. Cuadros-Vargas,
R. Romero, and K. Obermayer. Speeding up Algorithms of the SOM Family
for Large and High Dimensional Databases.
.
In Yamakawa T., editor, Proceedings WSOM, pages 167-172, 2003.
(FTP PDF, 244 kb)
In this paper, Spatial Access Methods, like R-Tree and k-d Tree,
for indexing data, are used to speed up the training process and performance
of data analysis methods which learning algorithms are kind of competitive
learning. Often, the search for the winning neuron is performed sequentially,
which leads to a large number of operations. Instead of using the common
sequential determination of the winning neuron, which has a computational
complexity of O(N) (where N is the number of candidate units to be the
winner), the approach proposed here allows to find the winning neuron in,
approximately, log N steps. Results obtained by incorporating k-d-tree,
R-Tree into Self-Organizing Maps are presented and compared with their
sequential counterpart implementation of SOM. The methods of SOM family used
are: k-means, Kohonen network and GNG network. Several database has been used
for demonstrating that a dramatic speed up can be achieved, what is very
significant when large-scale and high dimensional databases are being
considered.
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