PySOMVis framework involves popular Self-Organizing Maps visualization techniques, which is inspired by Java based SOMToolbox (http://www.ifs.tuwien.ac.at/dm/somtoolbox/index.html)
Pictures bellow are based on the projected Chain Link Data set (http://ifs.tuwien.ac.at/dm/somtoolbox/datasets.html). It is synthetic data representing two intertwined rings, which presents the topology violations after projection.
The SOM map represents 18x12 neurons trained 10000 times with learning rate 0.7 and sigma 7 in SOMToolbox.
 Activity Histogram |
 Cluster Connection |
 Component Plane |
 D-Matrix |
 Topology Error |
 U-Matrix |
 Graph based |
 Hit Histogram |
 Metro Map |
 Minimum Spanning Tree |
 U*-Matrix |
 Pie Chart |
 Quantization Error |
 Smoothed Data Histogram |
 Sky Metaphor |
 Chessboard |
 K-means (2 clusters) |
 average, complete, single, WARD |
SOMStreamVis approach helps to explore dynamic pattern with trained map. The example represents weather forecast, it includes 5 features of different temperature, pressure and wind speed values over 35 years (taken from the https://power.larc.nasa.gov/data-access-viewer/).
The features are following:
- RH2M - Relative Humidity at 2 Meters (%)
- PS - Surface Pressure (kPa)
- T2M - Temperature at 2 Meters (C)
- WS50M - Wind Speed at 50 Meters (m/s)
- ALLSKY_SFC8_LW_DWN - Downward Thermal Infrared (Longwave) Radiative Flux (kW-hr/m^2/day)
 SOMStreamVis interface with trajectory-based approach |
 Projection of 3 years (coloring is based on WARD clusterisation)  Projection of 35 years (coloring is based on WARD clusterisation) |
1. Sergei Mnishko and Andreas Rauber. Som visualization framework in python, including somstreamvis, a time series visualization. In Jan Faigl, Madalina Olteanu, and Jan Drchal, editors, Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization, pages 98–107, Cham, 2022. Springer International Publishing. DOI: https://doi.org/10.1007/978-3-031-15444-7_10