Scalable parallel algorithms for surface fitting and data mining

Date

2001

Authors

Christen, Peter
Hegland, Markus
Nielsen, Ole
Roberts, Stephen
Strazdins, Peter
Altas, I

Journal Title

Journal ISSN

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.

Description

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

Citation

Source

Parallel Computing

Type

Journal article

Book Title

Entity type

Access Statement

License Rights

Restricted until

2037-12-31