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

Ngan V.T. Nguyen, Tommy Dang

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

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.

Original languageEnglish
Title of host publicationAdvances in Visual Computing - 14th International Symposium on Visual Computing, ISVC 2019, Proceedings
EditorsGeorge Bebis, Bahram Parvin, Richard Boyle, Darko Koracin, Daniela Ushizima, Sek Chai, Shinjiro Sueda, Xin Lin, Aidong Lu, Daniel Thalmann, Chaoli Wang, Panpan Xu
PublisherSpringer
Pages330-341
Number of pages12
ISBN (Print)9783030337193
DOIs
StatePublished - 2019
Event14th International Symposium on Visual Computing, ISVC 2019 - Nevada, United States
Duration: Oct 7 2019Oct 9 2019

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11844 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference14th International Symposium on Visual Computing, ISVC 2019
Country/TerritoryUnited States
CityNevada
Period10/7/1910/9/19

Keywords

  • High-dimensional data analysis
  • Multivariate time series analysis
  • Parallel coordinates
  • Radar charts
  • Scatterplot matrix
  • t-distributed stochastic neighbor embedding

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