If an anomalous percent above 0 is specified, the percent of data that is furthest from the network (by Euclidian distance) is re-classified a second time on its own into a second SOM network of the same dimension such that there will be twice as many classes. The size of the neural network is the square-root of the number of classifications, which can be any of 4,6,16,25,36,49,etc… Two new channels are added (or replaced) in the database:Ĭlass – the classifications, 0 to number of classifications - 1ĮuD - Euclidian distance of the point from the assigned neuron class, which is the closest based on Euclidian distance. The report provides a simple visual impression of the classifications. The first three channels are used to colour-code the resulting SOM neural network report. In this implementation up to 16 separate data channels can be analysed. This GX analyses multivariate data by grouping data into statistically meaningful groupings using SOM neural-network analysis. If installed correctly you will see the SOM dialog:
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