@inproceedings{aa03cae75a21467db5c13e5b1ad420a2,
title = "RadarViewer: Visualizing the dynamics of multivariate data",
abstract = "This showcase presents a visual approach based on clustering and superimposing to construct a high-level overview of sequential event data while balancing the amount of information and the cardinality in it. We also implement an interactive prototype, called RadarViewer , that allows domain analysts to simultaneously analyze sequence clustering, extract useful distribution patterns, drill multiple levels-of-detail to accelerate the analysis. The RadarViewer is demonstrated through case studies with real-world temporal datasets of different sizes.",
keywords = "Radar chart, multivariate data analysis, time-series visualization",
author = "Ngan Nguyen and Jon Hass and Yong Chen and Jie Li and Alan Sill and Tommy Dang",
note = "Publisher Copyright: {\textcopyright} 2020 ACM.; null ; Conference date: 27-07-2020 Through 31-07-2020",
year = "2020",
month = jul,
day = "26",
doi = "10.1145/3311790.3404538",
language = "English",
series = "ACM International Conference Proceeding Series",
publisher = "Association for Computing Machinery",
pages = "555--556",
booktitle = "PEARC 2020 - Practice and Experience in Advanced Research Computing 2020",
}