Clustering Applications and Validation: A case study with the Kohonen
SOM

David Dubin
Graduate School of Library and Information Science
University of Illinois at Urbana-Champaign
Champaign, IL 61820
dubin@alexia.lis.uiuc.edu 

Daniel X. Pape
Community Architectures for Network Information Systems Laboratory
University of Illinois at Urbana-Champaign
Champaign, IL 61820
dpape@canis.uiuc.edu


We have developed an integrated suite of tools for conducting validation
studies on the popular self-organizing map. Through this development, we
have confronted issues that we believe shed light on the reluctance of
SOM researchers to adopt accepted cluster validation methods. SOM
researchers may object on the basis of scalability issues, or on the
naivete of the presupposition of a one-to-one mapping between clusters
and SOM cells.

We discuss the architecture of our system, its components (artificial
data generation, clustering, validation) and the protocols for sharing
data among them. Our design choices have the goal of affording scalable
and realistic evaluation studies. We believe we can contribute not only
a better understanding of the SOM's strengths and weaknesses, but also
data that can improve the SOM's usability as a tool for visualizing
multivariate data.