Fateh Tipu

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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)
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)
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 methodology based on (1) correlation analytics for identifying key drivers of(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)
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