We introduce a family of unsupervised algorithms, numerical taxonomy clustering, to simultaneously cluster data, and to learn a taxonomy that encodes the relationship between the ...
Large clusters of mutual dependence can cause problems for comprehension, testing and maintenance. This paper introduces the concept of coherent dependence clusters, techniques fo...
Syed S. Islam, Jens Krinke, David Binkley, Mark Ha...
Large clusters of mutual dependence have long been regarded as a problem impeding comprehension, testing, maintenance, and reverse engineering. An effective visualization can aid ...
This paper presents a novel algorithm combining view-dependent rendering and conservative occlusion culling for interactive display of complex environments. A vertex hierarchy of ...
We consider clustering situations in which the pairwise affinity between data points depends on a latent ”context” variable. For example, when clustering features arising fro...