Parsimonious Side Propagation (2007)
Bradley Mangasarian, P. S. Bradley, O. L. Mangasarian
A fast parsimonious linear-programming-based algorithm for training neural networks is proposed that suppresses redundant features while using a minimal number of hidden units. This is achieved by...
Constrained k-means clustering (2000)
P. S. Bradley, K. P. Bennett, A. Demiriz
We consider practical methods for adding constraints to the K-Means clustering algorithm in order to avoid local solutions with empty clusters or clusters having very few points. We often observe...
P.S. Bradley, O. L. Mangasarian
A finite new algorithm is proposed for clustering m given points in n-dimensional real space into k clusters by generating k planes that constitute a local solution to the nonconvex problem of...
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...
P. S. Bradley, O. L. Mangasarian, Panos Pardalos
. A finite new algorithm is proposed for clustering m given points in n-dimensional real space into k clusters by generating k planes that constitute a local solution to the nonconvex problem of...
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...
Refining Initial Points for K-Means Clustering (1998)
P. S. Bradley, Usama M. Fayyad
Practical approaches to clustering use an iterative procedure (e.g. K-Means, EM) which converges to one of numerous local minima. It is known that these iterative techniques are especially sensitive...
Massive Data Discrimination via Linear Support Vector Machines (1998)
P. S. Bradley, O. L. Mangasarian
A linear support vector machine formulation is used to generate a fast, finitely-terminating linear-programming algorithm for discriminating between two massive sets in n-dimensional space, where the...
Refining Initial Points for K-Means Clustering (1998)
Bradley Microsoft, Paul S. Bradley, P. S. Bradley, Usama M. Fayyad, Usama M. Fayyad
Practical approaches to clustering use an iterative procedure (e.g. K-Means, EM) which converges to one of numerous local minima. It is known that these iterative techniques are especially sensitive...
Feature Selection via Concave Minimization and Support Vector Machines (1998)
P.S. Bradley, O. L. Mangasarian
Computational comparison is made between two feature selection approaches for finding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as...
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...
Mathematical Programming for Data Mining: Formulations and Challenges (1998)
P. S. Bradley, Usama M. Fayyad, O. L. Mangasarian
This paper is intended to serve as an overview of a rapidly emerging research and applications area. In addition to providing a general overview, motivating the importance of data mining problems...
Feature selection via concave minimization and support vector machines (1998)
P. S. Bradley, O. L. Mangasarian
Computational comparison is made between two feature selection approaches for nding a separating plane that discriminates between two point sets in an n-dimensional feature space that utilizes as few...
Clustering via Concave Minimization (1997)
The problem of assigning m points in the n-dimensional real space R
Parsimonious Least Norm Approximation (1997)
P. S. Bradley, O. L. Mangasarian, J. B. Rosen
A theoretically justifiable fast finite successive linear approximation algorithm is proposed for obtaining a parsimonious solution to a corrupted linear system Ax = b + p, where the corruption p is...
Feature Selection via Mathematical Programming (1997)
P.S. Bradley, O. L. Mangasarian, W. N. Street
The problem of discriminating between two finite point sets in n-dimensional feature space by a separating plane that utilizes as few of the features as possible, is formulated as a mathematical...
Compressed Data Cubes for OLAPAggregate Query Approximation on Continuous Dimensions (1988)
Usama M. Fayyad, Paul S. Bradley, Jayavel Shanmugasundaram Usama, Jayavel Shanmugasundaram, Usama Fayyad, P. S. Bradley
Efficiently answering decision support queries is an important problem. Most of the work in this direction has been in the context of the data cube. Queries are efficiently answered by pre-computing...
Optimization Methods In Massive Datasets
P.S. Bradley, O. L. Mangasarian, D. R. Musicant
We describe the role of generalized support vector machines in separating massive and complex data using arbitrary nonlinear kernels. Feature selection that improves generalization is implemented via...