Scalable Parallel Algorithms For
Predictive Modelling
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
Related Book
Other papers in this volume
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