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

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Neural Information Processing (ICONIP 2017)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 10638))

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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 when conducting causal analysis of multivariate and nonlinear data. Based on the SOM 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 pattern change, it is important to measure the difference of the patterns. Many methods have been proposed for measuring the dissimilarity of patterns; however, it is still a major challenge to identify how patterns are different. In this paper, we propose a visual approach for analyzing changes to weighted SOM patterns. This approach extracts features that represent the difference of patterns by change and facilitates overall and detailed comparisons of pattern changes. Ecological data are used to demonstrate the usefulness of our approach and the experimental results show that it visualizes the change information effectively.

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Notes

  1. 1.

    The ecological features: B1 (Shredders), B2 (Filtering-Collectors), B3 (Collector-Gathers), B4 (Scrapers) and B5 (Predators) for Biological data set; P1 (Elevation), P2 (Slope), P3 (Stream Order), P4 (Embeddedness) and P5 (Water Temperature) for Physical data set. The feature values are all standardized for the total 130 data.

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Acknowledgments

This research was partially supported by HMR+ SPARC Implementation Funding (BMRI 2015) under the Project G181478 and ARC’s Discovery Project funding scheme (DP150101134).

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Authors and Affiliations

  1. School of IT, Faculty of Engineering and IT, The University of Sydney, Camperdown, NSW, 2006, Australia

    Younjin Chung, Joachim Gudmundsson & Masahiro Takatsuka

Authors
  1. Younjin Chung
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  2. Joachim Gudmundsson
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  3. Masahiro Takatsuka
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Corresponding author

Correspondence to Younjin Chung .

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Editors and Affiliations

  1. Guangdong University of Technology, Guangzhou, China

    Derong Liu

  2. Guangdong University of Technology, Guangzhou, China

    Shengli Xie

  3. South China University of Technology, Guangzhou, China

    Yuanqing Li

  4. Institute of Automation, Chinese Academy of Sciences, Beijing, China

    Dongbin Zhao

  5. King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

    El-Sayed M. El-Alfy

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Chung, Y., Gudmundsson, J., Takatsuka, M. (2017). A Visual Analysis of Changes to Weighted Self-Organizing Map Patterns. In: Liu, D., Xie, S., Li, Y., Zhao, D., El-Alfy, ES. (eds) Neural Information Processing. ICONIP 2017. Lecture Notes in Computer Science(), vol 10638. Springer, Cham. https://doi.org/10.1007/978-3-319-70139-4_24

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  • DOI: https://doi.org/10.1007/978-3-319-70139-4_24

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-70138-7

  • Online ISBN: 978-3-319-70139-4

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