Clustering Quality and Topology Preservation in Fast Learning SOMs
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Nonlinear dimensionality reduction by locally linear inlaying
IEEE Transactions on Neural Networks
Soft topographic maps for clustering and classifying bacteria using housekeeping genes
Advances in Artificial Neural Systems
A New Training Method for Large Self Organizing Maps
Neural Processing Letters
| Hi-index | 0.00 |
Several topology preservation measures and monitoring schemes have been proposed to help ascertain the correct organization of the self-organizing map (SOM) structure. Here, we consider a novel idea that performs faster than previous alternatives while showing interesting behavior in practice. Our proposal aims to facilitate inexpensive, online monitoring of topographic map formation algorithms.