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 |  Looking for Extreme Data Mining?

Klimasauskas Group specializes in the extreme aspects of data mining. When you hit a wall in deriving value from your data, or you want to make sure that you are getting the most out of it, Klimasauskas Group is the right place to come to. We specialize in pushing data-analytic technology to extremes in performance and reliability. We work on both commercial and classified government projects. Below are some recent examples of projects we have worked on:


Reliable Classifier Based on a Small Data Set.

This particular application involved predicting human behavior. There were typically 25-100 incidents which identify both the absence, or type of a specific behavior. Each incident included 40-90 features of the situation. Klimasauskas group developed an approach to building high performance statistically reliable models that maximized the use of input data, despite the small numbers of training cases. This same approach is applicable to a large number of situations which are input rich, data poor, and where accurate predictions have a high value augmenting human expertise. (See: Behavioral Applications)

Adaptive Market Profiling.

A proto-type system was developed to estimate sex, age, economic, and educational characteristics of a TV viewer based on the programs they watched. This used statistics from recorded viewing habits.in an on-line adaptive system that was able to differentiate between multiple viewers in a household, and adaptively refine the viewing profile. Results of the profile estimates could be used to insert specific advertisements or suggest other programs that may be of interest. Cross-marketing and cross-selling are other areas where this approach could be effective. (See: Marketing Applications)

Non-orthogonal Signal Decomposition.

A recursive procedure was developed to systematically identify interacting signals in a complex composite wave, and use these to synthesize a future prediction of selected elements of the signal. (See: Financial Applications)


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