Abstract
The U-matrix has become a standard visualization of self-organizing feature maps (SOM). Here we present the abstract U-matrix, which formalizes the structures on a U-matrix such that distance calculations between best-matching units w.r.t. the height structures of a U-matrix are precisely defined (U-cell distance). This enables the assessment of the topological correctness of the SOM and the implementation of clustering algorithms that take the structures seen on the U-matrix into account. A weighted Delaunay graph of the U-cell distances allows the calculation of a dendrogram corresponding to the structures of the U-matrix. The method is shown to detect and visualize meaningful cluster structures on difficult artificial and real-life data.
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Lötsch, J., Ultsch, A. (2014). Exploiting the Structures of the U-Matrix. In: Villmann, T., Schleif, FM., Kaden, M., Lange, M. (eds) Advances in Self-Organizing Maps and Learning Vector Quantization. Advances in Intelligent Systems and Computing, vol 295. Springer, Cham. https://doi.org/10.1007/978-3-319-07695-9_24
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DOI: https://doi.org/10.1007/978-3-319-07695-9_24
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