```@article{Garcke.Griebel.Thess:2000,
author = {J. Garcke and M. Griebel and M. Thess},
title = {Data mining with sparse grids},
year = {2001},
journal = {Computing},
volume = {67},
number = {3},
pages = {225--253},
note = {also as SFB 256 Preprint 675, Institut f\"ur Angewandte
Mathematik, Universit\"at Bonn, 2000},
ps = {http://wissrech.ins.uni-bonn.de/research/pub/garcke/datamine15.ps.gz 1},
abstract = {We present a new approach to the classification problem
arising in data mining. It is based on the regularization
network approach but, in contrast to the other methods
which employ ansatz functions associated to data points, we
use a grid in the usually high-dimensional feature space
for the minimization process. To cope with the curse of
dimensionality, we employ sparse grids. Thus, only
\$O(h_n^{-1} n^{d-1})\$ instead of \$O(h_n^{-d})\$ grid points
and unknowns are involved. Here \$d\$ denotes the dimension
of the feature space and \$h_n = 2^{-n} \$ gives the mesh
size. To be precise, we suggest to use the sparse grid
combination technique where the classification problem is
discretized and solved on a certain sequence of
conventional grids w ith uniform mesh sizes in each
coordinate direction. The sparse grid solution is then
obtained from the solutions on these different grids by
linear combination . In contrast to other sparse grid
techniques, the combination method is simpler to use and
can be parallelized in a natural an d straightforward way.

We describe the sparse grid combination technique for the
classification problem in terms of the regularization
network approach. We then give implementational d etails
and discuss the complexity of the algorithm. It turns out
that the metho d scales linear with the number of
instances, i.e. the amount of data to be clas sified.
Finally we report on the quality of the classifier build by
our new method. Here we consider standard test problems
from the UCI repository and problems wit h huge synthetical
data sets in up to 9 dimensions. It turns out that our new
method achieves correctness rates which are competitive to
that of the best existing methods.},
annote = {article,data}
}
```