Combination of Self Organizing Maps and Growing Neural Gas

Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F61989100%3A27240%2F14%3A86092011" target="_blank" >RIV/61989100:27240/14:86092011 - isvavai.cz</a>
Alternative codes found
RIV/61989100:27740/14:86092011
Result on the web
<a href="http://download.springer.com/static/pdf/454/chp%253A10.1007%252F978-3-662-45237-0_11.pdf?auth66=1426768124_c052bd06d912f86771f677478b164c4d&ext=.pdf" target="_blank" >http://download.springer.com/static/pdf/454/chp%253A10.1007%252F978-3-662-45237-0_11.pdf?auth66=1426768124_c052bd06d912f86771f677478b164c4d&ext=.pdf</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-3-662-45237-0_11" target="_blank" >10.1007/978-3-662-45237-0_11</a>

Result language
angličtina
Original language name
Combination of Self Organizing Maps and Growing Neural Gas
Original language description
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, whichis 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 clusteris presented in the paper. Some experimental results are also presented.
Czech name
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Czech description
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Project
Result was created during the realization of more than one project. More information in the Projects tab.
Continuities
P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year
2014
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

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