Parallel implementation of self-organizing maps
Self-Organizing neural networks
An efficient parallel algorithm for LISSOM neural network
Parallel Computing
Efficient Implementation of the THSOM Neural Network
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
GCCB'06 Proceedings of the 2006 international conference on Distributed, high-performance and grid computing in computational biology
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A large number of applications have shown that the self-organizing map is a prominent unsupervised neural network model for high-dimensional data analysis. However, the high execution times required to train the map put a limit to its use in many application domains, where either very large datasets are encountered and/or interactive response times are required. In order to provide interactive response times during data analysis we developed the parSOM, a software-based parallel implementation of the self-organizing map Parallel execution reduces the training time to a large degree, with an even higher speedup obtained by using the resulting cache effects. We demonstrate the scalability of the parSOM system and the speed-up obtained on different architectures using an example from high-dimensional text data classification.