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Ant-SNE: Tracking Community Evolution via Animated t-SNE

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Book cover Advances in Visual Computing (ISVC 2019)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11844))

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Abstract

We introduce a method for tracking the community evolution and a prototype (Ant-SNE) for analyzing multivariate time series and guiding interactive exploration through high-dimensional data. The method is based on t-distributed Stochastic Neighbor Embedding (t-SNE), a machine learning algorithm for nonlinear dimension reduction well-suited for embedding high-dimensional data for visualization in a low-dimensional space of two or three dimensions. By tracking the evolution of temporal multivariate data points, we are able to locate unusual behaviors (outliers) and interesting sub-series for further analysis. In the experiments, we conducted two case studies with the US employment dataset and the HPC health status dataset in order to confirm the effectiveness of the proposed system.

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Authors and Affiliations

  1. Texas Tech University, Lubbock, TX, 79409, USA

    Ngan V. T. Nguyen & Tommy Dang

Authors
  1. Ngan V. T. Nguyen
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  2. Tommy Dang
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Corresponding author

Correspondence to Ngan V. T. Nguyen .

Editor information

Editors and Affiliations

  1. University of Nevada, Reno, NV, USA

    George Bebis

  2. NASA Ames Research Center, Moffett Field, CA, USA

    Richard Boyle

  3. University of Nevada, Reno, NV, USA

    Bahram Parvin

  4. Desert Research Institute, Reno, NV, USA

    Darko Koracin

  5. Lawrence Berkeley National Laboratory, Berkeley, CA, USA

    Daniela Ushizima

  6. Latent AI, Palo Alto, CA, USA

    Sek Chai

  7. Texas A&M University, College Station, TX, USA

    Shinjiro Sueda

  8. Louisiana State University, Baton Rouge, LA, USA

    Xin Lin

  9. University of North Carolina at Charlotte, Charlotte, NC, USA

    Aidong Lu

  10. École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland

    Daniel Thalmann

  11. Notre Dame University, Notre Dame, IN, USA

    Chaoli Wang

  12. Bosch Research North America, Palo Alto, CA, USA

    Panpan Xu

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Nguyen, N.V.T., Dang, T. (2019). Ant-SNE: Tracking Community Evolution via Animated t-SNE. In: Bebis, G., et al. Advances in Visual Computing. ISVC 2019. Lecture Notes in Computer Science(), vol 11844. Springer, Cham. https://doi.org/10.1007/978-3-030-33720-9_25

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  • DOI: https://doi.org/10.1007/978-3-030-33720-9_25

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-33719-3

  • Online ISBN: 978-3-030-33720-9

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