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- Paul S. Bradley, Olvi L. Mangasarian
- ICML
- 1998

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 of the n features (dimensions) as possible. In the concave minimization approach [19, 5] a separating plane is generated by minimizing a weighted sum of… (More)

- Paul S. Bradley, Usama M. Fayyad
- ICML
- 1998

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 to initial starting conditions. We present a procedure for computing a refined starting condition from a given initial one that is based on an efficient… (More)

- Paul S. Bradley, Usama M. Fayyad, Cory Reina
- KDD
- 1998

Practical clustering algorithms require multiple data scans to achieve convergence. For large databases, these scans become prohibitively expensive. We present a scalable clustering framework applicable to a wide class of iterative clustering. We require at most one scan of the database. In this work, the framework is instantiated and numerically justified… (More)

- Paul S Bradley, William Sheldon, Blake Wooster, Peter Olsen, Paul Boanas, Peter Krustrup
- Journal of sports sciences
- 2009

The aims of this study were to (1) determine the activity profiles of a large sample of English FA Premier League soccer players and (2) examine high-intensity running during elite-standard soccer matches for players in various playing positions. Twenty-eight English FA Premier League games were analysed during the 2005-2006 competitive season (n=370),… (More)

- Paul S. Bradley, Olvi L. Mangasarian
- J. Global Optimization
- 2000

A nite 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 minimizing the sum of squares of the 2-norm distances between each point and a nearest plane. The key to the algorithm lies in a formulation that generates a plane in… (More)

- Cheng Yang, Usama M. Fayyad, Paul S. Bradley
- KDD
- 2001

We present a generalization of frequent itemsets allowing for the notion of errors in the itemset definition. We motivate the problem and present an efficient algorithm that identifies error-tolerant frequent clusters of items in transactional data (customer-purchase data, web browsing data, text, etc.). The algorithm exploits sparseness of the underlying… (More)

w. N. Street Computer Science Department Oklahoma State University 205 Mathematical Sciences Stillwater, OK 74078 email: nstreet@es. okstate. edu The problem of assigning m points in the n-dimensional real space Rn to k clusters is formulated as that of determining k centers in Rn such that the sum of distances of each point to the nearest center is… (More)

- Usama M. Fayyad, Cory Reina, Paul S. Bradley
- KDD
- 1998

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 for computing a refined starting condition from a given initial one that is based on an efficient technique for estimating the modes of a distribution. The… (More)

- Paul S. Bradley, Olvi L. Mangasarian, William Nick Street
- INFORMS Journal on Computing
- 1998

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 this phenomena when applying K-Means to datasets where the number of dimensions is n 10 and the number of desired clusters is k 20. We propose explicitly adding k… (More)