Abstract
The self-organizing map artificial neural network takes high-dimensional data and produces a diagram (map) that displays it in one or two dimensions. In short, humans can visualize interactions when displayed in one, two, or three dimensions, but not four or more dimensions. Data composed of only one variable can “see” a point on an x-axis diagram; data composed of two variables can be displayed on an x–y axis diagram; data composed of three variables can be displayed on an x–y–z axis diagram, and the visualization stops here. We simply cannot visualize diagrams in four or more dimensions, and that is where the self-organizing map comes into play. It has the ability of analyzing data in an unsupervised way (without any preconceived indication of the number of patters present in the data) and placing the resulting analysis in a one- or two-dimensional diagram. While some information might be lost in the translation is more than made up with the insights that one can glean from the resulting diagram.
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MASSINI G. (2007). SOM (Self Organizing Maps), Semeion Software #19, v. 7.
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Massini, G. (2013). Visualization and Clustering of Self-Organizing Maps. In: Buscema, M., Tastle, W. (eds) Intelligent Data Mining in Law Enforcement Analytics. Springer, Dordrecht. https://doi.org/10.1007/978-94-007-4914-6_11
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