Patch clustering for massive data sets

@article{Alex2009PatchCF,
  title={Patch clustering for massive data sets},
  author={Nikolai Alex and Alexander Hasenfuss and Barbara Hammer},
  journal={Neurocomputing},
  year={2009},
  volume={72},
  pages={1455-1469}
}
The presence of huge data sets poses new problems to popular clustering and visualization algorithms such as neural gas (NG) and the self-organising-map (SOM) due to memory and time constraints. In such situations, it is no longer possible to store all data points in the main memory at once and only a few, ideally only one run over the whole data set is still affordable to achieve a feasible training time. In this contribution we propose single pass extensions of the classical clustering… CONTINUE READING

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