|Title||Hierarchical Clustering for Volumetric Scalar Fields
(Masters Thesis) |
Christopher S. Co |
|School||Center for Image Processing and Integrated Computing, University of California, Davis|
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.