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Open Access Publications from the University of California

Hierarchical Clustering for Volumetric Scalar Fields


We present a flexible method by which large unstructured scalar fields can be represented in a simplified form. Using a parallelizable classification algorithm to build a cluster hierarchy, we generate a multiresolution representation of the original data. The method uses principal component analysis (PCA) for cluster classification and a fitting technique based on a set of radial basis functions. Once the cluster hierarchy has been generated, we utilize a variety of techniques for extracting different levels of resolution.

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