Growing Neural Gas – A Parallel Approach

Abstract

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 SOM or Growing Neural Gas (GNG). The learning phase of the ANN, which is time-consuming especially for large high-dimensional datasets, is the main drawback of this approach to data clustering. The parallel modification, Growing Neural Gas, and its implementation on the HPC cluster is presented in the paper. Some experimental results are also presented.