WIT Press


Scalable Parallel Algorithms For Predictive Modelling

Price

Free (open access)

Paper DOI

10.2495/DATA000411

Volume

25

Pages

10

Published

2000

Size

1,092 kb

Author(s)

P. Christen, M. Hegland, O. Nielsen, S. Roberts, I. Altas

Abstract

Data Mining applications have to deal with increasingly large data sets and complexity. Only algorithms which scale linearly with data size are feasible. We present parallel regression algorithms which after a few initial scans of the data compute predictive models for data mining and do not require further access to the data. In addition, we describe various ways of dealing with the complexity (high dimensionality) of the data. Three methods are presented for three different ranges of attribute numbers. They use ideas from the finite element method and are based on penalised least squares fits using sparse grids and additive mode

Keywords