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- Chidanand Apté, Eric Bibelnieks, Ramesh Natarajan, Edwin P. D. Pednault, Fateh Tipu, Deb Campbell +1 other
- KDD
- 2001

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)

The Data Abstraction Research Group was formed in the early 1990s, to bring focus to the Mathematical Sciences department's work 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, statistical… (More)

We describe two methodologies for obtaining segmented regression estimators from massive training data sets. The first methodology, called Linear Regression Tree (LRT), is used for continuous response variables, and the second and complementary methodology, called Naive Bayes Tree (NBT), is used for categorical response variables. These are implemented in… (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 a grid-based approach for enterprise-scale data mining that leverages database technology for I/O parallelism, and on-demand compute servers for compute parallelism in the statistical computations. By enterprise-scale, we mean the highly-automated use of data mining in vertical business applications, where the data is stored on one or more… (More)