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IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3
Parallel Clustering on a Commodity Supercomputer
Como, Italy
July 24-July 27
ISBN: 0-7695-0619-4
Giuseppe Patanè, University of Catania
Marco Russo, University of Messina
k -means based clustering algorithms have interesting performances in several application fields. The computational complexity of these techniques depends on the size of the data set and the codebook. The larger the data set and the codebook, the greater the computing time to reach the con vergence. This paper illustrates the behavior of t w o clustering algorithms we have realized and parallelized on a commodity supercomputer.
Index Terms:
Parallel, Clustering, Unsupervised Learning, GLA, LBG, ELBG
Citation:
Giuseppe Patanè, Marco Russo, "Parallel Clustering on a Commodity Supercomputer," ijcnn, vol. 3, pp.3575, IEEE-INNS-ENNS International Joint Conference on Neural Networks (IJCNN'00)-Volume 3, 2000
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