Self-organizing maps (SOMs), introduced by Teuvo Kohonen in the 1980s, are a powerful unsupervised learning technique used to visualize and interpret high-dimensional data by mapping it onto a lower-dimensional space, typically two dimensions.
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This article describes a workflow for using Self-Organizing Maps (SOM) as a time- and cost-efficient method of visualizing and validating property relationships ...
Jun 6, 2024 · Self organizing maps is a data visualization technique that clusters similar data together to make it easier to understand high-dimensional data.
Jul 9, 2019 · Self-Organising Maps (SOMs) are an unsupervised data visualisation technique that can be used to visualise high-dimensional data sets in ...
In this paper, we discuss about SOM method in the process visualization of dynamic systems. With a case example produced with the Olkiluoto plant data we show ...
Abstract. Based on the Self-Organizing Map (SOM) algorithm, development of effective solutions for visual analysis and retrieval in complex data is possible ...
A Self-organizing Map is a data visualization technique developed by Professor Teuvo Kohonen in the early 1980's.
A self-organizing map consists of two layers of neurons: an input layer and a so-called competition layer. The weights of the connections from the input neurons ...
May 26, 2019 · the purpose of SOM is that it's providing a data visualization technique that helps to understand high dimensional data by reducing the ...
The self-organizing map (SOM) is an efficient tool for visualization of multidimensional numerical data. In this paper, an overview and categorization of ...