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Speeding up Algorithms of the SOM Family for Large and High Dimensional Databases
Citation key Cuadros03
Author Cuadros-Vargas, E. and Romero, R. and Obermayer, K.
Title of Book Proceedings WSOM
Pages 167 – 172
Year 2003
Editor Yamakawa T.
Abstract 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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