Jaturon Chattratichat, John Darlington, Moustafa Ghanem, Yike Guo, Harald Huning, Martin Kohler, Janjao Sutiwaraphun, Hing Wing To, Dan Yang
Data mining over large data-sets is important due to its obvious commercial potential. However, it is also a major challenge due to its computational complexity. Exploiting the inherent parallelism of data mining algorithms provides a direct solution by utilising the large data retrieval and processing power of parallel architectures. In this paper, we present some results of our intensive research on parallelising data mining algorithms. In particular, we also present a methodology for determining the proper parallelisation strategy based on the idea of algorithmic skeletons and performance modelling. This research aims to provide a systematic way to develop parallel data mining algorithms and applications.