Umatrix-package: Umatrix-package

In Umatrix: Visualization of Structures in High-Dimensional Data

Umatrix-package

Description

The ESOM(emergent self organizing map) is an improvement of the regular SOM(self organizing map) which allows for toroid grids of neurons and is intended to be used in combination with the Umatrix. The set of neurons is referred to as weights within this package, as they represent the values within the high dimensional space. The neuron with smallest distance to a datapoint is called a Bestmatch and can be considered as projection of said datapoint. As the Umatrix is usually toroid, it is drawn four consecutive times to remove border effects. An island, or Imx, is a filter mask, which cuts out a subset of the Umatrix, which shows every point only a single time while avoiding border effects cutting through potential clusters. Finally the Pmatrix shows the density structures within the grid, by a set radius. It can be combined with the Umatrix resulting in the UStarMatrix, which is therefore a combination of density based structures as well as clearly divided ones.

References

Ultsch, A.: Data mining and knowledge discovery with emergent self-organizing feature maps for multivariate time series, In Oja, E. & Kaski, S. (Eds.), Kohonen maps, (1 ed., pp. 33-46), Elsevier, 1999.

Ultsch, A.: Maps for the visualization of high-dimensional data spaces, Proc. Workshop on Self organizing Maps (WSOM), pp. 225-230, Kyushu, Japan, 2003.

Ultsch, A.: U* C: Self-organized Clustering with Emergent Feature Maps, Lernen, Wissensentdeckung und Adaptivitaet (LWA), pp. 240-244, Saarbruecken, Germany, 2005.

Lotsch, J., Ultsch, A.: Exploiting the Structures of the U-Matrix, in Villmann, T., Schleif, F.-M., Kaden, M. & Lange, M. (eds.), Proc. Advances in Self-Organizing Maps and Learning Vector Quantization, pp. 249-257, Springer International Publishing, Mittweida, Germany, 2014.

Ultsch, A., Behnisch, M., Lotsch, J.: ESOM Visualizations for Quality Assessment in Clustering, In Merenyi, E., Mendenhall, J. M. & O'Driscoll, P. (Eds.), Advances in Self-Organizing Maps and Learning Vector Quantization: Proceedings of the 11th International Workshop WSOM 2016, pp. 39-48, Houston, Texas, USA, January 6-8, 2016, (10.1007/978-3-319-28518-4_3), Cham, Springer International Publishing, 2016.

Thrun, M. C., Lerch, F., Lotsch, J., Ultsch, A.: Visualization and 3D Printing of Multivariate Data of Biomarkers, in Skala, V. (Ed.), International Conference in Central Europe on Computer Graphics, Visualization and Computer Vision,Plzen, 2016.

Umatrix documentation built on Sept. 11, 2024, 8:09 p.m.