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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. 

The study of clouds, i.e., where they occur and what are their characteristics, plays a key role in the understanding of climate change. Clustering is a common machine learning technique used in atmospheric science to classify cloud types. Many parallelism techniques e.g., MPI, OpenMP and Spark, could achieve efficient and scalable clustering of large-scale satellite observation data. In order to understand their differences, this paper studies and compares three different approaches on parallel clustering of satellite observation data. Benchmarking experiments with k-means clustering are conducted with three parallelism techniques, namely OpenMP, OpenMP+MPI, and Spark, on a HPC cluster using up to 16 nodes.