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TimeCluster: dimension reduction applied to temporal data for visual analytics
The Visual Computer, Volume: 35, Issue: 6-8, Pages: 1013 - 1026
Swansea University Authors:
Mark Jones , Xianghua Xie
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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...
Published in: | The Visual Computer |
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ISSN: | 0178-2789 1432-2315 |
Published: |
2019
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URI: | https://cronfa.swan.ac.uk/Record/cronfa49701 |
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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 |
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2e1030b6e14fc9debd5d5ae7cc335562 b334d40963c7a2f435f06d2c26c74e11 |
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2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie |
author |
Mark Jones Xianghua Xie |
author2 |
Mohammed Ali Mark Jones Xianghua Xie Mark Williams |
format |
Journal article |
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The Visual Computer |
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35 |
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6-8 |
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1013 |
publishDate |
2019 |
institution |
Swansea University |
issn |
0178-2789 1432-2315 |
doi_str_mv |
10.1007/s00371-019-01673-y |
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Faculty of Science and Engineering |
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facultyofscienceandengineering |
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Faculty of Science and Engineering |
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Faculty of Science and Engineering |
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School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science |
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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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1763753122690236416 |
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11.021648 |