Applied multivariate statistical analysis
Applied multivariate statistical analysis
Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural fuzzy systems: a neuro-fuzzy synergism to intelligent systems
Neural networks for pattern recognition
Neural networks for pattern recognition
The process of knowledge discovery in databases
Advances in knowledge discovery and data mining
Bayesian classification (AutoClass): theory and results
Advances in knowledge discovery and data mining
Discovering informative patterns and data cleaning
Advances in knowledge discovery and data mining
Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
Selecting and reporting what is interesting
Advances in knowledge discovery and data mining
Data mining: building competitive advantage
Data mining: building competitive advantage
KDD process standards (panel session) (title only)
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Neural Networks: A Comprehensive Foundation
Neural Networks: A Comprehensive Foundation
Cached sufficient statistics for efficient machine learning with large datasets
Journal of Artificial Intelligence Research
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In this paper we present the up-to-now results of a federally funded research project that aims to develop a hybrid knowledge discovery framework which would support the excavation of valuable information from military health systems data repositories in order to deliver useful knowledge to patients, medical practitioners and policy planners. Across the military health system, medical data is being generated at a staggering rate. Effective ways to discover useful knowledge from these enormous volumes of data would be extremely valuable in making a significant difference in military healthcare systems. In many data rich environments (including private and public health care systems), data mining and its enabling technologies are emerging as a powerful means for researchers, practitioners, and consumers to more effectively gain and use knowledge. The challenge lies in finding new and innovative ways to deal with the increasing volume and complexity of medical data and thereby improve our understanding of disease and health related issues. So far in this project, we established the foundation for a knowledge discovery framework that has potential to overcome those challenges, developed a set of deployable data mining components, and implemented an integrated toolkit that maps those data mining components into the proposed framework.