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 F. 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… Expand

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. Expand
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. Expand
Growing cell structures--A self-organizing network for unsupervised and supervised learning
TLDR
A new self-organizing neural network model that has two variants that performs unsupervised learning and can be used for data visualization, clustering, and vector quantization is presented and results on the two-spirals benchmark and a vowel classification problem are presented that are better than any results previously published. Expand
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. Expand
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. Expand
Alternative Ways for Cluster Visualization in Self-Organizing Maps Dieter Merkl
  • Proceedings of the Workshop on Self-Organizing Maps (WSOM'97)
  • 1997
On the similarity of eagles, hawks, and cows { Visualization of similarity in self-organizing maps
  • Proc Int'l Workshop Fuzzy-Neuro-Systems'97
  • 1997
Cluster Visualization in Unsupervised Neural Networks
  • Cluster Visualization in Unsupervised Neural Networks
  • 1996
Cluster Visualization in Unsupervised Neural Networks. Diplomarbeit, Technische Universit
  • 1996
The e ect of lateral inhibition on learning speed and precision of a self-organizing map
  • In Proc Australian Conf on Neural Networks, Sydney, Australia,
  • 1995
...
1
2
...