A Visual Analysis of Changes to Weighted Self-Organizing Map Patterns

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.

Use of the self-organising map network (SOMNet) as a decision support system for regional mental health planning

“…Based on machine learning and information visualisation capabilities of the SOM, the SOMNet is developed for interactive visual data mining between multiple datasets [ 21 , 22 ]. The SOMNet learns the structural relationships between different datasets by its weight association.…”

“…An alternative is to combine artificial neuronal networks (ANNs) with advanced information visualisation techniques in a single DSS. Based on the demand, the aim of this study is to assess a new DSS model using a novel approach, a self-organising map network (SOMNet) [ 21 , 22 ], combined with the EbCA [ 11 ] for the use of knowledge-guided mental health planning. The SOMNet was developed to facilitate interactive visual data mining of complex data to enable domain experts to (1) generate and verify hypotheses; (2) express interest through the process of KDD; (3) enhance information transferring between analysts and decision-makers; (4) specify information processing and present outcomes of analytical reasoning processes; and (5) identify hidden information and elicit tacit knowledge that can be formalised and transformed into rules for further data analysis [ 23 – 25 ].…”