Abstract
Research data obtained during economics or human studies experiments often displays a complex distribution. Even in the two-dimensional case, the statistical identification of subgroups in research data poses an analytical challenge. Here we introduce an interactive R-based tool called “AdaptGauss2D”. It enables a valid identification of a meaningful multimodal structure in two-dimensional data. With a human-in-the-loop approach, a Gaussian mixture model (GMM) can be fitted to the data. The interactive interface allows a supervised selection of the number and parameters of the GMM based on various visualizations. Integrating a Human-in-the-loop into the process of modeling two-dimensional gaussian mixtures enables the expectation-maximization (EM) algorithm to adapt to more complex GMM compared to the standard non-interactive approach. The work demonstrates that the interactive modeling process for GMM improves the quality of the model in contrast to non-interactive modeling. The improvement is shown using the datasets of EngyTime and a large flow cytometry dataset. The R package “AdaptGauss2D” is available on GitHub https://github.com/Mthrun/AdaptGauss2D.
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Thrun, M.C., Stier, Q., Ultsch, A. (2023). Interactive Toolbox for Two-Dimensional Gaussian Mixture Modeling. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13718. Springer, Cham. https://doi.org/10.1007/978-3-031-26422-1_51
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DOI: https://doi.org/10.1007/978-3-031-26422-1_51
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