• Corpus ID: 11272432

Alternative Ways for Cluster Visualization inSelf-Organizing

@inproceedings{mapsDieter1997AlternativeWF,
  title={Alternative Ways for Cluster Visualization inSelf-Organizing},
  author={mapsDieter and Merkl and Andreas and RauberInstitut and Fachgebiet Softwaretechnik},
  year={1997}
}
We present two enhanced visualization techniques for the self-organizing map allowing the intuitive representation of input data similarity. The general idea of both approaches is to visualize the relationship of nodes to facilitate the detection of cluster boundaries without modifying the architecture or the basic training process of SOM. One approach mirrors the movement of weight vectors during the training process within a two-dimensional (virtual) output space, whereas the second results… 

References

SHOWING 1-10 OF 16 REFERENCES

On the Similarity of Eagles, Hawks, and Cows: Visualization of Semantic Similarity in Self-Organizin

TLDR
An extension to the self-organizing map learning rule is described, enabeling a straightforward visual representation of input data similarity in high-dimensional input structures that allows intuitive analysis of the similarities inherent in the input data and most important, intuitive recognition of cluster boundaries.

Incremental grid growing: encoding high-dimensional structure into a two-dimensional feature map

TLDR
In the proposed approach, nodes are added incrementally to a regular two-dimensional grid, which is drawable at all times, irrespective of the dimensionality of the input space, resulting in a map that explicitly represents the cluster structure of the high-dimensional input.

Kohonen Feature Maps and Growing Cell Structures - a Performance Comparison

TLDR
A performance comparison of two self-organizing networks, the Kohonen Feature Map and the recently proposed Growing Cell Structures, shows that the growing cell Structures exhibit significantly better performance by every criterion.

Script Recognition with Hierarchical Feature Maps

TLDR
The hierarchical feature map system recognizes an input story as an instance of a particular script by classifying it at three levels: scripts, tracks and role bindings, and serves as memory organization for script-based episodic memory.

Cluster Visualization in Unsupervised Neural Networks. Diplomarbeit, Technische Universit

  • 1996

Visualizing high-dimensional structure with the Incremen- tal Grid Growing network

  • In Proc Int'l Conference on Machine
  • 1995

Alternative Ways for Cluster Visualization in Self-Organizing Maps Dieter Merkl

  • Proceedings of the Workshop on Self-Organizing Maps (WSOM'97)
  • 1997

Self-organizing neural networks for visualization and classiication

  • Information and Classiication -Concepts, Methods and Applications
  • 1993

Self-organizing neural networks for visualization and classi cation. In Information and Classi cation - Concepts, Methods and Applications

  • 1993