  A self-organizing map (SOM) is a type of competitive artificial neural
network, which projects the high-dimensional input space of the training
samples into a low-dimensional space with the topology relations preserved.
This makes SOMs supportive of organizing and visualizing complex data sets and
have been pervasively used among numerous disciplines with different
applications. Notwithstanding its wide applications, the self-organizing map is
perplexed by its inherent randomness, which produces dissimilar SOM patterns
even when being trained on identical training samples with the same parameters
every time, and thus causes usability concerns for other domain practitioners
and precludes more potential users from exploring SOM based applications in a
broader spectrum. Motivated by this practical concern, we propose a
deterministic approach as a supplement to the standard self-organizing map. In
accordance with the theoretical design, the experimental results with satellite
cloud data demonstrate the effective and efficient organization as well as
simplification capabilities of the proposed approach.
