Data Mining Tools
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by Irfan Altas, Sergey Bakin, Markus Hegland, Steve Roberts, Berwin A. Turlach
http://www.stats.adelaide.edu.au/people/bturlach/cv/../psfiles/dm.tools.ps.gz
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Abstract:
In this paper, we discuss several scalable and parallel discovery and predictive data mining tools. They successfully address many of the computational challenges associated with the analysis of data sets with millions to billions of records and tens to hundreds of attributes. In particular, we describe parallel and scalable methods for additive models, approximate thin plate splines and adaptive regression splines. In addition we describe a method to discover symbolic descriptions of key areas of very large data sets.
Citations
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