Scaling EM (Expectation-Maximization) Clustering to Large Databases (1999)
Paul S. Bradley, Usama M. Fayyad, Cory A. Reina, P. S. Bradley, Usama Fayyad, Cory Reina
Practical statistical clustering algorithms typically center upon an iterative refinement optimization procedure to compute a locally optimal clustering solution that maximizes the fit to data. These...
Scaling EM (Expectation-Maximization) Clustering to Large Databases (1999)
P. S. Bradley, Usama Fayyad, Cory Reina, P. S. Bradley, Usama Fayyad, Cory Reina
: Practical statistical data clustering algorithms require multiple data scans to converge. For large databases, these scans become prohibitively expensive. We present a scalable clustering framework...
Scaling Clustering Algorithms to Large Databases (1998)
P. S. Bradley, Usama Fayyad, Cory Reina
Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clustering framework...
Scaling Clustering Algorithms to Large Databases (1998)
Paul S. Bradley, Bradley Usama Fayyad, Cory A. Reina, P. S. Bradley, Usama Fayyad, Cory Reina
Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clustering framework...
Initialization of Iterative Refinement Clustering Algorithms (1998)
Usama Fayyad, Cory Reina, P. S. Bradley
Iterative refinement clustering algorithms (e.g. K-Means, EM) converge to one of numerous local minima. It is known that they are especially sensitive to initial conditions. We present a procedure...
Initialization of Iterative Refinement Clustering Algorithms (1998)
Usama M. Fayyad, Cory A. Reina, Paul S. Bradley, Usama Fayyad, Cory Reina, P. S. Bradley
Iterative refinement clustering algorithms (e.g. K-Means, EM) converge to one of numerous local minima. It is known that they are especially sensitive to initial conditions. We present a procedure...