TitleHierarchical Clustering for Volumetric Scalar Fields (Masters Thesis)
Author(s) Christopher S. Co
Year 2002
SchoolCenter for Image Processing and Integrated Computing, University of California, Davis
Abstract 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.