Abstract
The unique power of the Self-Organizing Map (SOM) lies specifically in the wide range of visualizations available to support explorative data analysis. However, these are usually scattered across independent libraries, making their integration and comparative application in data exploration difficult. Additionally, few visualizations focus on the dynamics of data forming a time series, while the importance of understanding data evolution is gaining importance in many settings. In this paper, we thus present SOMStreamVis, an extension to a visualization specifically tuned to capture and represent time-series information on self-organizing maps. We furthermore provide an PySOMVis, an open-source software framework realized in Python that integrates SOMStreamVis with a wide range of additional visualizations of the SOM, making it a powerful data exploration tool.
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Mnishko, S., Rauber, A. (2022). SOM Visualization Framework in Python, Including SOMStreamVis, a Time Series Visualization. In: Faigl, J., Olteanu, M., Drchal, J. (eds) Advances in Self-Organizing Maps, Learning Vector Quantization, Clustering and Data Visualization. WSOM+ 2022. Lecture Notes in Networks and Systems, vol 533. Springer, Cham. https://doi.org/10.1007/978-3-031-15444-7_10
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