Abstract
This paper applies the recent advances of visual analytics, which combine computers’ and humans’ strengths to the data exploration process, to alleviate the scalability and overplotting issues of dimensional projection techniques for high-dimensional temporal datasets. Our approach first uses clustering algorithms to select the representative data points at each time step for each data profile. We then apply dimension reduction techniques to visualize the temporal relationships via connecting lines. Finally, we propose a couple of different underlying models to treat time steps and the time dimension to mitigate the final projections’ visual clutter. We built a web-based prototype, called MultiProjector, to integrate these components into a unified data exploration process. The prototype is validated on several high-dimensional temporal datasets in various application domains to demonstrate our approach’s benefits.
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Dang, T., Nguyen, N.V.T. (2022). MultiProjector: Temporal Projection for Multivariates Time Series. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2022. Lecture Notes in Computer Science, vol 13598. Springer, Cham. https://doi.org/10.1007/978-3-031-20713-6_7
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