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• W1870625491 abstract "To cluster increasingly massive data sets that are common today in data and text mining, we propose a parallel implementation of the k-means clustering algorithm based on the message passing model. The proposed algorithm exploits the inherent data-parallelism in the k-means algorithm. We analytically show that the speedup and the scaleup of our algorithm approach the optimal as the number of data points increases. We implemented our algorithm on an IBM POWERparallel SP2 with a maximum of 16 nodes. On typical test data sets, we observe nearly linear relative speedups, for example, 15.62 on 16 nodes, and essentially linear scaleup in the size of the data set and in the number of clusters desired. For a 2 gigabyte test data set, our implementation drives the 16 node SP2 at more than 1.8 gigaflops." @default.
• W1870625491 created "2016-06-24" @default.
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• W1870625491 creator A5063718511 @default.
• W1870625491 date "2002-01-01" @default.
• W1870625491 modified "2023-10-16" @default.
• W1870625491 title "A Data-Clustering Algorithm on Distributed Memory Multiprocessors" @default.
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• W1870625491 doi "https://doi.org/10.1007/3-540-46502-2_13" @default.
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