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The UPA (Underwriting Profitability Analysis) application embodies a new approach to mining Property & Casualty (P&C) insurance policy and claims data for the purpose of constructing predictive models for insurance risks. UPA utilizes the ProbE (Probabilistic Estimation) predictive modeling data mining kernel to discover risk characterization rules by(More)
Fingerhut Business Intelligence (BI) has a long and successful history of building statistical models to predict consumer behavior. The models constructed are typically segmentation-based models in which the target audience is split into subpopulations (i.e., customer segments) and individually tailored statistical models are then developed for each(More)
Multiple congenital cardiac defects are usually addressed by cardiac surgery. We present our experience with simultaneous transcatheter treatment of multiple defects in children. Ten children, six females and four males, with multiple defects underwent treatment with interventional technique. The mean age was 4.4 ± 2.6 years (range, 7 months to 8 years).(More)
IBM ProbE (for probabilistic estimation) is an extensible, embeddable, and scalable modeling engine, particularly well-suited for implementing segmentation-based modeling techniques, wherein data records are partitioned into segments and separate predictive models are developed for each segment. We describe the ProbE framework and discuss two key business(More)
We describe an automated system for improving yield, power consumption and speed characteristics in the manufacture of semiconductors. Data are continually collected in the form of a history of tool usage, electrical and other real-valued measurements—a dimension of tens of thousands of features. Unique to this approach is the inference of patterns in the(More)
The Data Abstraction Research Group was formed in the early 1990s, to bring focus to the work of the Mathematical Sciences Department in the emerging area of knowledge discovery and data mining (KD & DM). Most activities in this group have been performed in the technical area of predictive modeling, roughly at the intersection of machine learning,(More)
The UPA (Underwriting Profitability Analysis) application embodies a new approach to mining Property & Casualty (P&C) insurance policy and claims data for the purpose of constructing predictive models for insurance risks. UPA utilizes the ProbE (Probabilistic Estimation) predictive modeling class library to discover risk characterization rules by analyzing(More)
N. Abe R. Akkiraju S. Buckley M. Ettl P. Huang D. Subramanian F. Tipu To compete and thrive in a changing business environment, a business can adapt by initiating and successfully carrying out business transformation projects. In this paper we propose a methodology for the optimal selection of such transformational projects. We propose a two-stage(More)
A methodology for embedding predictive modeling algorithms in a commercial parallel database is described; specifically, the parallel editions of IBM DB2 Universal Database, although many aspects of the overall approach can be used with other commercial parallel databases. This parallelization approach was implemented in the Version 8.2 release of DB2(More)
We have developed a computer program that computes and displays current-dipole solutions to the neuromagnetic inverse problem. The three-dimensional positions, orientations, and corresponding magnetic field values of the detection coils can be visualized with respect to a realistic representation of the subject's head shape. The topographies of the(More)
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