Discovering Knowledge in Data: An Introduction to Data Mining, 2nd Edition

Chapter 11Kohonen Networks

1. 11.1 Self-Organizing Maps
2. 11.2 Kohonen Networks
3. 11.3 Example of a Kohonen Network Study
4. 11.4 Cluster Validity
5. 11.5 Application of Clustering Using Kohonen Networks
6. 11.6 Interpreting the Clusters
7. 11.7 Using Cluster Membership as Input to Downstream Data Mining Models
   1. The R Zone
   2. References
   3. Exercises
   4. Hands-On Analysis

11.1 Self-Organizing Maps

Kohonen networks were introduced in 1982 by Finnish researcher Tuevo Kohonen [1]. Although applied initially to image and sound analysis, Kohonen networks are nevertheless an effective mechanism for clustering analysis. Kohonen networks represent a type of self-organizing map (SOM), which itself represents a special class of neural networks, which we studied in Chapter 9.

The goal of self-organizing maps is to convert a complex high-dimensional input signal into a simpler low-dimensional discrete map [2]. Thus, SOMs are nicely appropriate for cluster analysis, where underlying hidden patterns among records and fields are sought. SOMs structure the output nodes into clusters of nodes, where nodes in closer proximity are more similar to each other than to other nodes that are farther apart. Ritter [3] has shown that SOMs represent a nonlinear generalization of principal component analysis, another dimension-reduction technique.

SOMs are based on competitive learning, where the output nodes compete among themselves to be the winning node (or neuron), the only node to be activated by a particular input observation. ...