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TimeCluster: dimension reduction applied to temporal data for visual analytics

Mohammed Ali, Mark Jones Orcid Logo, Xianghua Xie Orcid Logo, Mark Williams

The Visual Computer, Volume: 35, Issue: 6-8, Pages: 1013 - 1026

Swansea University Authors: Mark Jones Orcid Logo, Xianghua Xie Orcid Logo

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DOI (Published version): 10.1007/s00371-019-01673-y

Abstract

With the increase of temporal data, there is a growing need for advanced solutions which assist users to understand such data, observe its changes over the time, find repeated patterns, detect outliers, and effectively label data instances in long time-series data. Although these tasks are quite dis...

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Published in: The Visual Computer
ISSN: 0178-2789 1432-2315
Published: 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa49701
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first_indexed 2019-05-07T15:46:05Z
last_indexed 2020-11-28T04:04:07Z
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spelling 2020-11-27T11:58:05.6116398 v2 49701 2019-03-24 TimeCluster: dimension reduction applied to temporal data for visual analytics 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2019-03-24 SCS With the increase of temporal data, there is a growing need for advanced solutions which assist users to understand such data, observe its changes over the time, find repeated patterns, detect outliers, and effectively label data instances in long time-series data. Although these tasks are quite distinct, and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system. Journal Article The Visual Computer 35 6-8 1013 1026 0178-2789 1432-2315 31 12 2019 2019-12-31 10.1007/s00371-019-01673-y COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University 2020-11-27T11:58:05.6116398 2019-03-24T19:21:07.7708006 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Mohammed Ali 1 Mark Jones 0000-0001-8991-1190 2 Xianghua Xie 0000-0002-2701-8660 3 Mark Williams 4 0049701-13052019102102.pdf TimeClusteronlinefirstversion.pdf 2019-05-13T10:21:02.2270000 Output 4309142 application/pdf Version of Record true 2019-05-13T00:00:00.0000000 Released under the terms of a Creative Commons Attribution 4.0 International License (CC-BY). true eng
title TimeCluster: dimension reduction applied to temporal data for visual analytics
spellingShingle TimeCluster: dimension reduction applied to temporal data for visual analytics
Mark Jones
Xianghua Xie
title_short TimeCluster: dimension reduction applied to temporal data for visual analytics
title_full TimeCluster: dimension reduction applied to temporal data for visual analytics
title_fullStr TimeCluster: dimension reduction applied to temporal data for visual analytics
title_full_unstemmed TimeCluster: dimension reduction applied to temporal data for visual analytics
title_sort TimeCluster: dimension reduction applied to temporal data for visual analytics
author_id_str_mv 2e1030b6e14fc9debd5d5ae7cc335562
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Mark Jones
Xianghua Xie
author2 Mohammed Ali
Mark Jones
Xianghua Xie
Mark Williams
format Journal article
container_title The Visual Computer
container_volume 35
container_issue 6-8
container_start_page 1013
publishDate 2019
institution Swansea University
issn 0178-2789
1432-2315
doi_str_mv 10.1007/s00371-019-01673-y
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description With the increase of temporal data, there is a growing need for advanced solutions which assist users to understand such data, observe its changes over the time, find repeated patterns, detect outliers, and effectively label data instances in long time-series data. Although these tasks are quite distinct, and are usually tackled separately, we present an interactive visual analytics system and approach that can address these issues in a single system. It enables users to visualize, understand and explore univariate or multivariate long time-series data in one image using a connected scatter plot. It supports interactive analysis and exploration for pattern discovery and outlier detection. Different dimensionality reduction techniques are used and compared in our system. Because of its power of extracting features, deep learning is used for multivariate time-series along with 2D reduction techniques for rapid and easy interpretation and interaction with large amount of time-series data. We deploy our system with different time-series datasets and report two real-world case studies that are used to evaluate our system.
published_date 2019-12-31T04:00:53Z
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score 11.037581

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