of Continuous-Valued Attributes Generation (2008)
Usama M. Fayyad, Keki B. Irani
Abstract. We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing...
Analysis of Digital POSS-II Catalogs Using Hierarchical Unsupervised Learning Algorithms (2007)
Jungsoon Yoo, Er Gray, Joseph Roden, Usama M. Fayyad, S. G. Djorgovski
. We apply techniques from the field of machine learning/ artificial intelligence to the problem of inducing meaningful structure from astronomical data. Cobweb/95 is a concept formation system that...
Knowledge Discovery in Large Image Databases: Dealing with Uncertainties in Ground Truth (2004)
Smyth, Padhraic, Burl, Michael C., Fayyad, Usama M., Perona, Pietro
This paper discusses the problem of knowledge discovery in image databases with particular focus on the issues which arise when absolute ground truth is not available.
Information Visualisation in Data Mining and Knowledge Discovery (2002)
Fayyad, Usama M. (ed.), Grinstein, Georges G. (ed.), Wierse, Andreas (ed.)
En este libro se conjugan dos ramas nuevas de la inteligencia artificial: la administración de la información a través de "data mining" se relaciona con la representación visual de información,...
Fayyad, Usama M. (ed.), Grinstein, Georges G. (ed.), Wierse, Andreas (ed.)
En este libro se conjugan dos ramas nuevas de la inteligencia artificial: la administración de la información a través de "data mining" se relaciona con la representación visual de información,...
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...
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...
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...
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...
Initialization of iterative refinement clustering algorithms (1998)
Usama M. Fayyad, Cory A. Reina, Paul 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...
Advances in knowledge discovery and data minig / Ed. de U.M. Fayyad...[et al.]. (1996)
Incluye índice
Retrofitting Decision Tree Classifiers Using Kernel Density Estimation (1995)
Padhraic Smyth, Alexander Gray, Er Gray, Usama M. Fayyad
A novel method for combining decision trees and kernel density estimators is proposed. Standard classification trees, or class probability trees, provide piecewise constant estimates of class...
On the Handling of Continuous-Valued Attributes in Decision Tree Generation (1992)
We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued...
On the handling of continuous-valued attributes in decision tree generation (1992)
Fayyad, Usama M., Irani, Keki B.
We present a result applicable to classification learning algorithms that generate decision trees or rules using the information entropy minimization heuristic for discretizing continuous-valued...
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...