Abstract
A method for visual clustering of large N-dimensional data sets is presented briefly. Its implementation on HP/Convex SPP/1600 enables visualization of data sets consisting of more than 104 multidimensional data vectors. The method was tested in PVM, MPI and data parallel environments. In the paper, the authors compare the parallel algorithm performance for these three interfaces. The results of tests, made to exemplify the algorithm “immunity” from data errors, are presented and discussed.
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Błasiak, J., Dzwinel, W. (1998). Visual clustering of multidimensional and large data sets using parallel environments. In: Sloot, P., Bubak, M., Hertzberger, B. (eds) High-Performance Computing and Networking. HPCN-Europe 1998. Lecture Notes in Computer Science, vol 1401. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0037167
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DOI: https://doi.org/10.1007/BFb0037167
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