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Scalable Parallel Algorithms for Surface Fitting and Data Mining

By Peter Christen, Markus Hegland, Ole M. Nielsen, Stephen Roberts, Peter E. Strazdins and Irfan Altas

Abstract

This paper presents parallel scalable algorithms for high dimensional surface fitting and predictive modelling which can be used in data mining applications. The presented algorithms are based on techniques like finite elements, thin plate splines, additive models and wavelets. They consist of two phases: First, data is read from secondary storage and a linear system is assembled. Secondly, the linear system is solved. The assembly can be done with almost no communication and the size of the linear system is independent of the data size. Thus the presented algorithms are both scalable with the data size and the number of processors. Key words: Data Mining, Thin Plate Splines, Additive Models, Wavelets, Parallel Linear System 1 Introduction In the last few years there has been an explosive growth in the amount of data being collected. The computerisation of business transactions and the use of bar codes in commercial outlets has provided businesses with enormous amounts of data. In s..

Topics: Key words, Data Mining, Thin Plate Splines, Additive Models, Wavelets, Parallel Linear System
Year: 2000
OAI identifier: oai:CiteSeerX.psu:10.1.1.32.1041
Provided by: CiteSeerX
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