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A self-organizing map (SOM) is a type of competitive artificial neuralnetwork, which projects the high-dimensional input space of the trainingsamples into a low-dimensional space with the topology relations preserved.This makes SOMs supportive of organizing and visualizing complex data sets andhave been pervasively used among numerous disciplines with differentapplications. Notwithstanding its wide applications, the self-organizing map isperplexed by its inherent randomness, which produces dissimilar SOM patternseven when being trained on identical training samples with the same parametersevery time, and thus causes usability concerns for other domain practitionersand precludes more potential users from exploring SOM based applications in abroader spectrum. Motivated by this practical concern, we propose adeterministic approach as a supplement to the standard self-organizing map. Inaccordance with the theoretical design, the experimental results with satellitecloud data demonstrate the effective and efficient organization as well assimplification capabilities of the proposed approach.

A Deterministic Self-Organizing Map Approach and its Application on Satellite Data based Cloud Type Classification