Scalable Parallel Algorithms for Surface Fitting and Data Mining
Peter Christen, Markus Hegland, Ole M. Nielsen, Stephen Roberts,
Peter E. Strazdins and Irfan Altas,
Scalable Parallel Algorithms for Surface Fitting and Data Mining
,
submitted Jun 2000,
accepted Sep 2000 to the Journal of Parallel Computing,
Special Issue on Solving Large Linear Systems.
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 size of the linear system is independent of the data size
and the assembly can be done with almost no communication. Thus the
presented algorithms are both scalable with the data size and the number
of processors.
Contents
Keywords
data mining, thin plate splines, additive models,
wavelets, parallel linear systems, symmetric indefinite systems