Abstract
Large amount of data collected daily requires efficient algorithms for its processing. The SUBDUE data mining system discovers substructures in structurally complex data, based on the minimum description length principle. Its parallel implementation, MPI-SUBDUE, was created in 2001 to reduce computation time and/or to deal with larger datasets. In this paper, a new, more efficient implementation of MPI-SUBDUE is introduced. The experimental results show that, for the mutagenesis dataset, the new implementation outperforms the original one by up to 33% and that the performance gain increases with the number of processors used.
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© 2006 Springer-Verlag Berlin Heidelberg
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Cai, M., Jonyer, I., Paprzycki, M. (2006). Improving Parallelism in Structural Data Mining. In: Wyrzykowski, R., Dongarra, J., Meyer, N., Waśniewski, J. (eds) Parallel Processing and Applied Mathematics. PPAM 2005. Lecture Notes in Computer Science, vol 3911. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11752578_55
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DOI: https://doi.org/10.1007/11752578_55
Publisher Name: Springer, Berlin, Heidelberg
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