Skip to main content

Paths of Wellbeing on Self-Organizing Maps

  • Conference paper
Advances in Self-Organizing Maps

Abstract

In this article, we introduce the concept of pathways of wellbeing and examine how such paths can be discovered from large data sets using the self-organizing map. Data sets used in the illustrative experiments include measurements of physical fitness and subjective assessments related to diagnosing work stress.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Basara, H., Yuan, M.: Community health assessment using self-organizing maps and geographic information systems. International Journal of Health Geographics 7(1), 67+ (2008)

    Article  Google Scholar 

  2. Cattinelli, I., Bolzoni, E., Barbieri, C., Mari, F., Martin-Guerrero, J., Soria-Olivas, E., Martinez-Martinez, J., Gomez-Sanchis, J., Amato, C., Stopper, A., Gatti, E.: Use of self-organizing maps for balanced scorecard analysis to monitor the performance of dialysis clinic chains. Health Care Management Science 15(1), 79–90 (2012)

    Article  Google Scholar 

  3. Cottrell, M., Bodt, E.D., Grégoire, P.: Financial application of the self-organizing map. In: Proceedings of EUFIT 1998, 6th European Congress on Intelligent Techniques Soft Computing, vol. 1, pp. 205–209 (1998)

    Google Scholar 

  4. Eklund, T., Back, B., Vanharanta, H., Visa, A.: Using the self-organizing map as a visualization tool in financial benchmarking. Information Visualization 2(3), 171–181 (2003)

    Article  Google Scholar 

  5. Erickson, K.I., Voss, M.W., Shaurya, R., Basak, C., Szabo, A., Chaddock, L., Kim, J.S., Heo, S., Alves, H., White, S.M., Wojcicki, T.R., Mailey, E., Vieira, V.J., Martin, S.A., Pence, B.D., Woods, J.A., McAuley, E., Kramer, A.F.: Exercise training increases size of hippocampus and improves memory. Proceedings of the National Academy of Sciences 108(7), 3017–3022 (2011)

    Article  Google Scholar 

  6. Gaetano, L., Di Benedetto, G., Tura, A., Balestra, G., Montevecchi, F.M., Kautzky-Willer, A., Pacini, G., Morbiducci, U.: A self-organizing map based morphological analysis of oral glucose tolerance test curves in women with gestational diabetes mellitus. Stud. Health Technol. Inform. 160(pt. 2), 1145–1149 (2010)

    Google Scholar 

  7. Honkela, T., Izzatdust, Z., Lagus, K.: Text Mining for Wellbeing: Selecting Stories Using Semantic and Pragmatic Features. In: Villa, A.E.P., Duch, W., Érdi, P., Masulli, F., Palm, G. (eds.) ICANN 2012, Part II. LNCS, vol. 7553, pp. 467–474. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  8. Honkela, T., Koskinen, I., Koskenniemi, T., Karvonen, S.: Kohonen’s Self-Organizing Map in Contextual Analysis of Data. In: Information Organization and Databases: Foundations of Data Organization, pp. 135–148. Kluwer (2000)

    Google Scholar 

  9. Kiviluoto, K.: Predicting bankruptcies with the self-organizing map. Neurocomputing 21(1-3), 191–201 (1998)

    Article  MATH  Google Scholar 

  10. Kohonen, T.: Self-Organizing Maps, 3rd edn. Springer (2001)

    Google Scholar 

  11. Lagus, K.: Map of WSOM 1997 abstracts - alternative index. In: Proceedings of WSOM 1997, Workshop on Self-Organizing Maps, pp. 4–6. Helsinki University of Technology, Neural Networks Research Centre (1997)

    Google Scholar 

  12. Lagus, K., Honkela, T., Kaski, S., Kohonen, T.: Self-organizing maps of document collections: A new approach to interactive exploration. In: Simoudis, E., Han, J., Fayyad, U. (eds.) Proceedings of the Second International Conference on Knowledge Discovery and Data Mining, pp. 238–243. AAAI Press (1996)

    Google Scholar 

  13. Lendasse, A., Lee, J.A., Wertz, V., Verleysen, M.: Forecasting electricity consumption using nonlinear projection and self-organizing maps. Neurocomputing 48(1-4), 299–311 (2002)

    Article  MATH  Google Scholar 

  14. Mabruk, A.F., Yousif, J.H.: Self-organizing map approach for identifying mental disorders. International Journal of Computer Applications 45(7), 25–30 (2012)

    Google Scholar 

  15. McGaugh, M.: A practical application of self-organizing maps in public health. In: 1st International Conference on Innovation and Entrepreneurship in Health. Oklahoma State University (2012)

    Google Scholar 

  16. Mehmood, Y., Abbas, M., Chen, X., Honkela, T.: Self-Organizing Maps of Nutrition, Lifestyle and Health Situation in the World. In: Laaksonen, J., Honkela, T. (eds.) WSOM 2011. LNCS, vol. 6731, pp. 160–167. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  17. Oja, M., Sperber, G.O., Blomberg, J., Kaski, S.: Self-organizing map-based discovery and visualization of human endogenous retroviral sequence groups. Int. J. Neural Syst. 15(3), 163–179 (2005)

    Article  Google Scholar 

  18. Paju, P., Malmi, E., Honkela, T.: Text Mining and Qualitative Analysis of an IT History Interview Collection. In: Impagliazzo, J., Lundin, P., Wangler, B. (eds.) History of Nordic Computing 3. IFIP AISC, vol. 350, pp. 433–443. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  19. Resta, M.: Assessing the Efficiency of Health Care Providers: A SOM Perspective. In: Laaksonen, J., Honkela, T. (eds.) WSOM 2011. LNCS, vol. 6731, pp. 30–39. Springer, Heidelberg (2011)

    Chapter  Google Scholar 

  20. Shove, E., Pantzar, M., Watson, M.: The dynamics of social practice: Everyday life and how it changes. Sage (2012)

    Google Scholar 

  21. Shvartz, E., Reibold, R.C.: Aerobic fitness norms for males and females aged 6 to 75 years: a review. Aviat. Space Environ. Med. 61(1), 3–11 (1990)

    Google Scholar 

  22. Simula, O., Vesanto, J., Vasara, P.: Analysis of industrial systems using the self-organizing map. In: Proceedings of KES 1998, Knowledge-Based Intelligent Electronic Systems, pp. 61–68 (1998)

    Google Scholar 

  23. Vatanen, T., Heikkilä, M., Honkela, T., Kettunen, O., Lagus, K., Pantzar, M.: Kuntotiedot kartalle - erilaiset hyvä- ja huonokuntoisten ryhmät näkyviin. Liikunta & Tiede (Sports & Science), pp. 48–53 (2012)

    Google Scholar 

  24. Vatanen, T., Paukkeri, M.S., Nieminen, I.T., Honkela, T.: Analyzing authors and articles using keyword extraction, self-organizing map and graph algorithms. In: Proceedings of the AKRR 2008, pp. 105–111 (2008)

    Google Scholar 

  25. Wosiski-Kuhn, M., Stranahan, A.M.: Opposing effects of positive and negative stress on hippocampal plasticity over the lifespan. Ageing Research Reviews 11(3), 399–403 (2011)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Krista Lagus .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lagus, K. et al. (2013). Paths of Wellbeing on Self-Organizing Maps. In: Estévez, P., Príncipe, J., Zegers, P. (eds) Advances in Self-Organizing Maps. Advances in Intelligent Systems and Computing, vol 198. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35230-0_35

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-35230-0_35

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-35229-4

  • Online ISBN: 978-3-642-35230-0

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics