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Erschienen in:
2015 | OriginalPaper | Buchkapitel
Parallel Canopy Clustering on GPUs
verfasst von : Yusuke Kozawa, Fumitaka Hayashi, Toshiyuki Amagasa, Hiroyuki Kitagawa
Erschienen in: Database and Expert Systems Applications
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Abstract
Canopy clustering is a preprocessing method for standard clustering algorithms such as k-means and hierarchical agglomerative clustering. Canopy clustering can greatly reduce the computational cost of clustering algorithms. However, canopy clustering itself may also take a vast amount of time for handling massive data, if we naïvely implement it. To address this problem, we present efficient algorithms and implementations of canopy clustering on GPUs, which have evolved recently as general-purpose many-core processors. We not only accelerate the computation of original canopy clustering, but also propose an algorithm using grid index. This algorithm partitions the data into cells to reduce redundant computations and, at the same time, to exploit the parallelism of GPUs. Experiments show that the proposed implementations on the GPU is 2 times faster on average than multi-threaded, SIMD implementations on two octa-core CPUs.