Abstract
This paper describes an approach for visualization of patterns in large data sets. The data sets are combined from external exposure and internal stress factors on human health. For deduction of modes of action on human health, external and internal stress factors have to be combined and classified. The approach shown in this paper is based upon clustering algorithms. Relationships between cases ban be obtained by visual inspection of clustering results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Kohonen, T.: Self Organizing Maps. Series in Computer Sciences. Springer, Heidelberg (1997)
Zampighi, L.M., Kavanau, C.L., Zampighi, C.A.: The Kohonen self-organizing map: a tool for the clustering and alignment of single particles imaged using random conical tilt. Journal of Structural Biology 146, 368–380 (2004)
Kohonen, T.: Self-organized formation of topologically correct feature maps. Biological Cybernetics 43, 59–69 (1982)
Kohonen, T., Kaski, S., Lagus, K., Salojärvi, J., Honkela, J., Paatero, V., Saarela, A.: Self organizing of a massive document collection. IEEE Transactions on Neural Networks 11, 574–585 (2000)
Rhee, J.I., Lee, K.I., Kim, C.K., Yim, Y.S., Chung, S.W., Wei, J., Bellgardt, K.H.: Classification of two-dimensional fluorescence sprectra using self-organizing maps. Biochemical Engineering Journal 22, 135–144 (2005)
Joliffe, I.T.: Discarding variables in a principal component analysis I: artificial data. Applied Statistics 21, 373–374 (1972)
Joliffe, I.T.: Discarding variables in a principal component analysis II: real data. Applied Statistics 22, 21–23 (1973)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Roeder, S., Rolle-Kampczyk, U., Herbarth, O. (2006). Visualization of Depending Patterns in Metabonomics. In: King, I., Wang, J., Chan, LW., Wang, D. (eds) Neural Information Processing. ICONIP 2006. Lecture Notes in Computer Science, vol 4234. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893295_32
Download citation
DOI: https://doi.org/10.1007/11893295_32
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46484-6
Online ISBN: 978-3-540-46485-3
eBook Packages: Computer ScienceComputer Science (R0)