Abstract
Self-organising maps (SOMs) have been applied in many different areas of science. In a typical application, large numbers of objects (thousands or more) are mapped to a two-dimensional grid of units in such a way that very similar objects end up in the same unit, and that neighbouring units are more similar than far-away units. The similarities of the individual units can be used in visualisation of the data by choosing appropriate colour schemes. Examples from image segmentation will show the usefulness of this approach. Often, additional information is available, e.g., class information, or measurements of a different nature. To take this extra information into account, we have extended the basic principle of SOMs to accommodate extra layers, one for each data modality. The closest unit is then given by a weighted sum of per-layer distances. The result is an overall better mapping, incorporating all available information. This is implemented in an R package “kohonen”.
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Acknowledgements
Thanh N. Tran (now Schering-Plough) and Dirk Hoekman (Wageningen University) are acknowledged for the SAR data and valuable discussions. The MRI data are obtained in the EC-funded INTERPRET project, and were recorded at the Radiology department of the Radboud University Medical Center (prof. Heerschap).
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Wehrens, R. (2009). Self-Organising Maps for Image Segmentation. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_34
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DOI: https://doi.org/10.1007/978-3-642-01044-6_34
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