# A Visual Measure of Changes to Weighted Self-Organizing Map Patterns

## TL;DR

This paper introduces a visualization method for effectively measuring and analyzing changes in weighted Self-Organizing Map patterns, aiding causal analysis of multivariate nonlinear data.

## Contribution

It proposes a novel visualization approach that combines colors and star glyphs to compare pattern differences in weighted SOMs, simplifying change analysis.

## Key findings

- Effective visualization of pattern changes demonstrated on ecological data
- Approach provides clear change information through integrated visual cues
- Method enhances understanding of output pattern variations in causal analysis

## Abstract

Estimating output changes by input changes is the main task in causal analysis. In previous work, input and output Self-Organizing Maps (SOMs) were associated for causal analysis of multivariate and nonlinear data. Based on the association, a weight distribution of the output conditional on a given input was obtained over the output map space. Such a weighted SOM pattern of the output changes when the input changes. In order to analyze the change, it is important to measure the difference of the patterns. Many methods have been proposed for the dissimilarity measure of patterns. However, it remains a major challenge when attempting to measure how the patterns change. In this paper, we propose a visualization approach that simplifies the comparison of the difference in terms of the pattern property. Using this approach, the change can be analyzed by integrating colors and star glyph shapes representing the property dissimilarity. Ecological data is used to demonstrate the usefulness of our approach and the experimental results show that our approach provides the change information effectively.