Self-organising Mapping Networks (som) with Sas E-miner
نویسنده
چکیده
Self –Organising mapping networks (SOM) (Kohonen, 2001) is a specific family of neural networks uses unsupervised training. In unsupervised training no target output is provided and the network evolves until stabilisation. SOM can be used for data visualisation, clustering, estimation, vector projection and a variety of other purposes. It is an effective modelling tool for the visualisation of high dimensional data. Non linear statistical relationships between high dimensional data are converted into simple geometric relationships of their image points on a low dimensional display, usually a two dimensional grid of nodes. The SOM inspired by the way in which various human sensory impressions neurologically mapped into the brain such the spatial or other relationship between stimuli corresponds to spatial relationships among the neurons
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تاریخ انتشار 2012