This paper describes the design and implementation on MIMD parallel machines of P-AutoClass, a parallel version of the AutoClass system based upon the Bayesian method for determini...
Abstract. Clustering is a problem of great practical importance in numerous applications. The problem of clustering becomes more challenging when the data is categorical, that is, ...
Grid computing is becoming an important framework for enabling applications to utilize widely distributed collections of computational and data resources, however current grid sof...
Paul D. Coddington, Lici Lu, Darren Webb, Andrew L...
Abstract. Clustering is a widely used unsupervised data analysis technique in machine learning. However, a common requirement amongst many existing clustering methods is that all p...
Shared machines in opportunistic grids typically have large quantities of unused disk space. These resources could be used to store application and checkpointing data when the mac...