Scalable parallel algorithms for surface fitting and data mining
Date
2001
Authors
Christen, Peter
Hegland, Markus
Nielsen, Ole
Roberts, Stephen
Strazdins, Peter
Altas, I
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Volume Title
Publisher
Elsevier
Abstract
This paper presents scalable parallel algorithms for high-dimensional surface fitting and predictive modelling which are used in data mining applications. These algorithms are based on techniques like finite elements, thin plate splines, wavelets and additive models. They all consist of two steps: 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.
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Keywords
Keywords: Computational complexity; Computational geometry; Curve fitting; Data mining; Data structures; Finite element method; Mathematical models; Parallel algorithms; Problem solving; Storage allocation (computer); Wavelet transforms; Additive models; Thin plate Additive models; Data mining; Parallel linear system solver; Thin plate splines; Wavelets
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Source
Parallel Computing
Type
Journal article
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2037-12-31
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