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Optimization of Combining of Self Organizing Maps and Growing Neural Gas

机译:优化自组织地图和种植神经气体的组合

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摘要

The paper deals with the issue of high dimensional data clustering. 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). 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. We focus on effective preprocessing for GNG. The clustering is realized on the output layer of SOM and the data for GNG are distributed into parallel processes.
机译:本文涉及高维数据聚类问题。聚类这种数据的一种可能方法是基于人工神经网络(ANN),例如越来越多的神经气体(GNG)或自组织地图(SOM)。并行改性,通过自组织地图预处理生长神经气体,并在纸上提出了HPC集群的实现。还提出了一些实验结果。我们专注于GNG的有效预处理。群集在SOM的输出层上实现,并且GNG的数据分布到并行过程中。

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