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Speedup of Fuzzy Clustering Through Stream Processing on Graphics Processing Units

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3 Author(s)
Anderson, D.T. ; Dept. of Electr. & Comput. Eng., Univ. of Missouri, Columbia, MO ; Luke, R.H. ; Keller, J.M.

As the number of data points, feature dimensionality, and number of centers for clustering algorithms increase, computational tractability becomes a problem. The fuzzy c-means has a large degree of inherent algorithmic parallelism that modern CPU architectures do not exploit. Many pattern recognition algorithms can be sped up on a graphics processing unit (GPU) as long as the majority of computation at various stages and the components are not dependent on each other. We present a generalized method for offloading fuzzy clustering to a GPU, while maintaining control over the number of data points, feature dimensionality, and the number of cluster centers. GPU-based clustering is a high-performance low-cost solution that frees up the CPU. Our results show a speed increase of over two orders of magnitude for particular clustering configurations and platforms.

Published in:

Fuzzy Systems, IEEE Transactions on  (Volume:16 ,  Issue: 4 )