The naive Bayesian classifier provides a simple and effective approach to classifier learning, but its attribute independence assumption is often violated in the real world. A numb...
It is common in classification methods to first place data in a vector space and then learn decision boundaries. We propose reversing that process: for fixed decision boundaries, ...
We present a divide-and-merge methodology for clustering a set of objects that combines a top-down "divide" phase with a bottom-up "merge" phase. In contrast, ...
David Cheng, Santosh Vempala, Ravi Kannan, Grant W...
Clustering, or unsupervised classification, has many uses in fields that depend on grouping results from large amount of data, an example being the N-body cosmological simulation ...
We investigate the management of flocking mobile objects using a parallel message-passing computer cluster. An octree, a data structure well-known for use in managing a 3D space, ...