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The growing hierarchical self-organizing map: exploratory analysis of high-dimensional data

机译:不断增长的分层自组织图:高维数据的探索性分析

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The self-organizing map (SOM) is a very popular unsupervised neural-network model for the analysis of high-dimensional input data as in data mining applications. However, at least two limitations have to be noted, which are related to the static architecture of this model as well as to the limited capabilities for the representation of hierarchical relations of the data. With our novel growing hierarchical SOM (GHSOM) we address both limitations. The GHSOM is an artificial neural-network model with hierarchical architecture composed of independent growing SOMs. The motivation was to provide a model that adapts its architecture during its unsupervised training process according to the particular requirements of the input data. Furthermore, by providing a global orientation of the independently growing maps in the individual layers of the hierarchy, navigation across branches is facilitated. The benefits of this novel neural network are a problem-dependent architecture and the intuitive representation of hierarchical relations in the data. This is especially appealing in explorative data mining applications, allowing the inherent structure of the data to unfold in a highly intuitive fashion.
机译:自组织映射(SOM)是一种非常流行的无监督神经网络模型,用于在数据挖掘应用程序中分析高维输入数据。但是,必须注意至少两个限制,这些限制与该模型的静态体系结构以及表示数据的层次关系的有限功能有关。通过我们不断发展的新型分层SOM(GHSOM),我们解决了这两个限制。 GHSOM是一个人工神经网络模型,具有由独立增长的SOM组成的分层体系结构。动机是提供一个模型,以根据输入数据的特定要求在其无监督的训练过程中适应其体系结构。此外,通过在层次结构的各个层中提供独立增长的地图的全局方向,可以促进跨分支的导航。这种新颖的神经网络的好处是问题相关的体系结构和数据中层次关系的直观表示。这在探索性数据挖掘应用程序中特别有吸引力,它允许以高度直观的方式展现数据的固有结构。

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