A scalable parallel FEM surface fitting algorithm for data mining

The development of automatic techniques to process and detect patterns in very large data sets is a major task in data mining. An essential subtask is the interpolation of surfaces, which can be done with multivariate regression. Thin plate splines provide a very good method to determine an approximating surface. Unfortunately, obtaining standard thin plate splines requires the solution of a dense linear system of order n, where n is the number of observations. Thus, standard thin plate splines are not practical, as the number of observations for data mining applications is often in the millions. We have developed a finite element approximation of a thin plate spline that can handle data sizes with millions of records. Each observation record has to be read from an external file once only and there is no need to store the data in memory. The resolution of the finite element method can be chosen independently from the number of data records. An overlapping domain partitioning is applied to achieve parallelism. Our algorithm is scalable both in the number of data points as well as with the number of processors. We present first results on a Sun shared-memory multiprocessor