SELF-ORGANIZING MAP BASED VISUALIZATION TECHNIQUES AND THEIR ASSESSMENT

Authors

  • Miki Sirola
  • Jaakko Talonen

DOI:

https://doi.org/10.47839/ijc.11.2.554

Keywords:

Self-organizing map, data analysis, neural methods, visualization.

Abstract

Our research group has been studying data-analysis based techniques in decision support and visualization. We had a long industrial research project in co-operation with a Finnish nuclear power plant Olkiluoto. We developed many decision support schemes based on Self-Organizing Map (SOM) method combined with other methodologies. Also several visualizations based on variou s data-analysis methods were developed. Data from the Olkiluoto plant and training simulator was used in the analysis. In this paper some of these visualizations are presented, analyzed, and assessed with a psychological framework. Measuring the information value of the visualizations is a real challenge. The developed visualizations and visualization techniques are also compared with some existing visualizations and techniques in current plants and research laboratories. The visualizations and the visualization techniques are developed further, and completely new visualizations and techniques are developed. We point out what additional value the new visualization techniques can produce. A detailed test case of using Self-Organizing Map (SOM) method with Olkiluoto plant data is presented. With this practical example the information value of this method is shown, and it is also pointed out how it can be assessed, and what are the most reliable criteria in this assessment.

References

M. Sirola, J. Talonen, J. Parviainen, G. Lampi, Decision support with data-analysis methods in a nuclear power plant, TKK Reports in Information and Computer Science, TKK-ICS-R29, Espoo, 2010, 23 p.

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Published

2014-08-01

How to Cite

Sirola, M., & Talonen, J. (2014). SELF-ORGANIZING MAP BASED VISUALIZATION TECHNIQUES AND THEIR ASSESSMENT. International Journal of Computing, 11(2), 96-103. https://doi.org/10.47839/ijc.11.2.554

Issue

2012, Volume 11, Issue 2

Section

Articles

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