Self-organization and associative memory: 3rd edition
Self-organization and associative memory: 3rd edition
Introduction to algorithms
A Nonlinear Mapping for Data Structure Analysis
IEEE Transactions on Computers
Visualising clusters in self-organising maps with minimum spanning trees
ICANN'10 Proceedings of the 20th international conference on Artificial neural networks: Part II
Visual Interpretation of Self Organizing Maps
SBRN '10 Proceedings of the 2010 Eleventh Brazilian Symposium on Neural Networks
Quantifying the neighborhood preservation of self-organizing feature maps
IEEE Transactions on Neural Networks
Automatic Cluster Detection in Kohonen's SOM
IEEE Transactions on Neural Networks
| Hi-index | 0.00 |
The Self Organizing Map (SOM) [1] proposed by Kohonen has proved to be remarkable in terms of its range of applications. It can be used for high dimensional space visualization, pattern recognition, input space dimensionality reduction and for generating prototyping to extrapolate information. Basically, tasks conducted by the SOM method are closely related with input space mapping in order to preserve topological and metric relationship between samples. These maps are meant to create a low dimensional output representation of high dimensional input space. Although maps higher than two dimensions can be created by SOM, it is common to work with the limit of one or two dimensions. This work presents a methodology named SOMM (Self-Organized Manifold Mapping) that can be useful to discover structures and clusters of input dataset using the SOM map as a representation of data distribution structure.