Abstract
High-dimensional time series analysis through visual techniques poses many challenges due to the visualization solutions proposed until now for exploratory tasks are not well-oriented to high volume of data. When the data sets grow large, the visual alternatives do not allow for a good association between similar time series. With the aim to increase more alternatives, we introduce a visual analytic approach based on Neighbor-Joining similarity tree. The proposed approach internally consists of five time series dimension reduction techniques widely used, two well-known similarity measures and interaction mechanisms to do exploratory analysis of high-dimensional time series data interactively.
Original language | English |
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Title of host publication | 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
Pages | 325-330 |
Number of pages | 6 |
ISBN (Electronic) | 9781538646625 |
DOIs | |
State | Published - 18 Jun 2018 |
Externally published | Yes |
Event | 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 - Bilbao, Spain Duration: 18 Dec 2017 → 20 Dec 2017 |
Publication series
Name | 2017 IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 |
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Conference
Conference | 17th IEEE International Symposium on Signal Processing and Information Technology, ISSPIT 2017 |
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Country/Territory | Spain |
City | Bilbao |
Period | 18/12/17 → 20/12/17 |
Bibliographical note
Publisher Copyright:© 2017 IEEE.