Divisive parallel clustering for multiresolution analysis

Clustering is a classical data analysis technique that is applied to a wide range of applications in the sciences and engineering. For very large data sets, the performance of a clustering algorithm becomes critical. Although clustering has been thoroughly studied over the last decades, little has been done on utilizing modern multi-processor machines to accelerate the analysis process. We propose a scalable clustering technique that benefits from existing parallel computers and networks of workstations. It supports the creation of multiresolution representation for very large geometric data sets. The output of the clustering process can be used for interactive data exploration, useful for view-dependent rendering, user-guided refinement, and progressive transmission