  Approximation of scattered data is often a task in many engineering problems.
The Radial Basis Function (RBF) approximation is appropriate for large
scattered datasets in d-dimensional space. It is non-separable approximation,
as it is based on a distance between two points. This method leads to a
solution of overdetermined linear system of equations.
  In this paper a new approach to the RBF approximation of large datasets is
introduced and experimental results for different real datasets and different
RBFs are presented with respect to the accuracy of computation. The proposed
approach uses symmetry of matrix and partitioning matrix into blocks.
