The clustering algorithm DBSCAN relies on a density-based notion of clusters and is designed to discover clusters of arbitrary shape as well as to distinguish noise. In this paper,...
—Algorithmic enhancements are described that enable large computational reduction in mean square-error data clustering. These improvements are incorporated into a parallel data-c...
As commodity microprocessors and networks reach performance levels comparable to those used in massively parallel processors, clusters of symmetric multiprocessors are starting to...
Heterogeneous networks of workstations have rapidly become a cost-effective computing solution in many application areas. This paper develops several highly innovative parallel al...
Antonio Plaza, David Valencia, Soraya Blazquez, Ja...
Clusters are now composed of non-uniform nodes with different CPUs, disks or network cards so that customers can adapt the cluster configuration to the changing technologies and t...
Tobias Mayr, Philippe Bonnet, Johannes Gehrke, Pra...