Graph Based Representations of Density Distribution and Distances for Self-Organizing Maps

@article{Tasdemir2010GraphBR,
  title={Graph Based Representations of Density Distribution and Distances for Self-Organizing Maps},
  author={Kadim Tasdemir},
  journal={IEEE Transactions on Neural Networks},
  year={2010},
  volume={21},
  pages={520-526}
}
The self-organizing map (SOM) is a powerful method for manifold learning because of producing a 2-D spatially ordered quantization of a higher dimensional data space on a rigid lattice and adaptively determining optimal approximation of the (unknown) density distribution of the data. However, a postprocessing visualization scheme is often required to capture the data manifold. A recent visualization scheme CONNvis, which is shown effective for clustering, uses a topology representing graph that… CONTINUE READING

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