Abstract
The present work explores nonparametric clustering algorithm HCA-MS. The combination of grid-based approach and Mean shift procedure allows the algorithm to discover arbitrary shaped clusters and to process large datasets, such as images. Parallel implementation of the HCA-MS algorithm on NVIDIA GPU using CUDA platform is presented. Provided experimental results on model data and multispectral images confirm the efficiency of the considered algorithm and its parallel implementation. The computation speedup on images was shown to be about 20x compared to 4 core CPU.
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Rylov, S.A. (2019). Parallel Implementation of Nonparametric Clustering Algorithm HCA-MS on GPU Using CUDA. In: Shokin, Y., Shaimardanov, Z. (eds) Computational and Information Technologies in Science, Engineering and Education. CITech 2018. Communications in Computer and Information Science, vol 998. Springer, Cham. https://doi.org/10.1007/978-3-030-12203-4_19
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DOI: https://doi.org/10.1007/978-3-030-12203-4_19
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