Abstract
This paper describes the realization of a parallel version of the k/h-means clustering algorithm. This is one of the basic algorithms used in a wide range of data mining tasks. We show how a database can be distributed and how the algorithm can be applied to this distributed database. The tests conducted on a network of 32 PCs showed for large data sets a nearly ideal speedup. © Springer-Verlag Berlin Heidelberg 1999.
Cite
CITATION STYLE
Stoffel, K., & Belkoniene, A. (1999). Parallel k/h-means clustering for large data sets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 1685 LNCS, pp. 1451–1454). Springer Verlag. https://doi.org/10.1007/3-540-48311-x_205
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