Cluster coloring of the Self-Organizing Map: An information visualization perspective

TL;DR

This paper introduces a flexible, perceptually accurate cluster coloring method for Self-Organizing Maps that enhances visualization of data structures without depending on specific projection techniques.

Contribution

It proposes a simple, customizable color space and a projection-independent coloring method to improve SOM visualizations from an information visualization perspective.

Findings

Color space is easy to construct and customize.

Method is modular and adaptable to various projection objectives.

Effective visualization demonstrated on iris and welfare datasets.

Abstract

This paper takes an information visualization perspective to visual representations in the general SOM paradigm. This involves viewing SOM-based visualizations through the eyes of Bertin's and Tufte's theories on data graphics. The regular grid shape of the Self-Organizing Map (SOM), while being a virtue for linking visualizations to it, restricts representation of cluster structures. From the viewpoint of information visualization, this paper provides a general, yet simple, solution to projection-based coloring of the SOM that reveals structures. First, the proposed color space is easy to construct and customize to the purpose of use, while aiming at being perceptually correct and informative through two separable dimensions. Second, the coloring method is not dependent on any specific method of projection, but is rather modular to fit any objective function suitable for the task at hand. The cluster coloring is illustrated on two datasets: the iris data, and welfare and poverty indicators.