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5 novembre 2014
- handle: 10670/1.cls4ih
- hal: hal-01405567
- doi: 10.1007/978-3-662-45237-0_11
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info:eu-repo/semantics/altIdentifier/doi/10.1007/978-3-662-45237-0_11
http://creativecommons.org/licenses/by/ , info:eu-repo/semantics/OpenAccess
Mots-clés
self organizing maps growing neural gas high-dimensional dataset high performance computingSujets proches
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Lukáš Vojáček et al., « Combination of Self Organizing Maps and Growing Neural Gas », HAL-SHS : sciences de l'information, de la communication et des bibliothèques, ID : 10.1007/978-3-662-45237-0_11
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Résumé
The paper deals with the high dimensional data clustering problem. One possible way to cluster this kind of data is based on Artificial Neural Networks (ANN) such as Growing Neural Gas (GNG) or Self Organizing Maps (SOM). The learning phase of ANN, which is time-consuming especially for large high-dimensional datasets, is the main drawback of this approach to data clustering. Parallel modification, Growing Neural Gas with pre-processing by Self Organizing Maps, and its implementation on the HPC cluster is presented in the paper. Some experimental results are also presented.