科研之友
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摘要
Despite the benefits of the Gustafson-Kessel (GK) clustering algorithm, it becomes computationally inefficient when applied to high-dimensional data. In this letter, a parallel implementation of the GK algorithm on the GPU with CUDA is proposed. Using an optimized matrix multiplication algorithm with fast access to shared memory, the CUDA version achieved a maximum 240-fold speedup over the single-CPU version.
- 出版日期2012-4
