Scalable Parallel Algorithms for Surface Fitting and Data Mining

This paper presents parallel scalable algorithms for high dimensional surface fitting and predictive modelling which can be used in data mining applications. The presented algorithms are based on techniques like finite elements, thin plate splines, additive models and wavelets. They consist of two phases: First, data is read from secondary storage and a linear system is assembled. Secondly, the linear system is solved. The size of the linear system is independent of the data size and the assembly can be done with almost no communication. Thus the presented algorithms are both scalable with the data size and the number of processors.