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The Gaussian mixture model (GMM) is a widely used probabilistic clustering model. The incremental learning algorithm of GMM is the basis of a variety of complex incremental learning algorithms. It is typically applied to real-time or massive data problems where the standard Expectation Maximum (EM) algorithm does not work. But the output of the incremental learning algorithm may exhibit degraded cluster...
As the size and complexity of scientific problems and datasets grow, scientists from a broad range of discipline areas are relying more and more on computational methods and simulations to help solve their problems. This paper presents a summary of heterogeneous algorithms and applications that have been developed by a large research organization (CSIRO) for solving practical and challenging science...
Scientists often need to extract, visualize and analyze lines from vast amounts of data to understand dynamic structures and interactions. The effectiveness of such a visual validation and analysis process mainly relies on a good strategy to categorize and visualize the lines. However, the sheer size of line data produced by state-of-the-art scientific simulations poses great challenges to preparing...
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