Uncovering hierarchical structure in data using the growing hierarchical self-organizing map

@article{Dittenbach2002UncoveringHS,
  title={Uncovering hierarchical structure in data using the growing hierarchical self-organizing map},
  author={Michael Dittenbach and Andreas Rauber and Dieter Merkl},
  journal={Neurocomputing},
  year={2002},
  volume={48},
  pages={199-216}
}
Abstract Discovering the inherent structure in data has become one of the major challenges in data mining applications. It requires stable and adaptive models that are capable of handling the typically very high-dimensional feature spaces. In particular, the representation of hierarchical relations and intuitively visible cluster boundaries are essential for a wide range of data mining applications. Current approaches based on neural networks hardly fulfill these requirements within a single… CONTINUE READING
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