A Distributed and Approximated Nearest Neighbors Algorithm for an Efficient Large Scale Mean Shift Clustering

@article{Beck2019ADA,
  title={A Distributed and Approximated Nearest Neighbors Algorithm for an Efficient Large Scale Mean Shift Clustering},
  author={Ga{\"e}l Beck and Tarn Duong and Mustapha Lebbah and Hanene Azzag and Christophe C{\'e}rin},
  journal={ArXiv},
  year={2019},
  volume={abs/1902.03833}
}
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21 Citations
Background Citations
2
Methods Citations
3

21 Citations

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References

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Two further algorithmic improvements are introduced: a normal scale (NS) choice of the optimal number of nearest neighbours, and locality sensitive hashing (LSH) to approximate nearest neighbour searches to offer the potential for an efficient method for Big Data Clustering.
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