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TimeCluster: dimension reduction applied to temporal data for visual analytics / Mohammed Ali; Mark W. Jones; Xianghua Xie; Mark Williams

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

Swansea University Author: Xie, Xianghua

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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/cronfa50944
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first_indexed 2019-06-26T20:54:06Z
last_indexed 2019-07-18T21:37:00Z
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spelling 2019-07-18T15:07:57Z v2 50944 2019-06-26 TimeCluster: dimension reduction applied to temporal data for visual analytics Xianghua Xie Xianghua Xie true 0000-0002-2701-8660 false b334d40963c7a2f435f06d2c26c74e11 53b7e8cec1e3c035df428f36f80bdea5 ulOdsUw0nzyNlMFzZoDyVp320YwKTXZRCaAvm14NMEw= 2019-06-26 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 0 0 2019 2019-01-01 10.1007/s00371-019-01673-y College of Science Computer Science CSCI SCS Visual Computing None 2019-07-18T15:07:57Z 2019-06-26T17:38:04Z College of Science Computer Science Mohammed Ali 1 Mark W. Jones 2 Xianghua Xie 3 Mark Williams 4 0050944-26062019173846.pdf TimeClusteronlinefirstversionv2.pdf 2019-06-26T17:38:46Z Output 4309142 application/pdf VoR true Updated Notes 09/07/2019 2019-06-26T00:00:00 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
Xie, Xianghua
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 b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv b334d40963c7a2f435f06d2c26c74e11_***_Xie, Xianghua
author Xie, Xianghua
author2 Mohammed Ali
Mark W. 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 College of Science
hierarchytype
hierarchy_top_id collegeofscience
hierarchy_top_title College of Science
hierarchy_parent_id collegeofscience
hierarchy_parent_title College of Science
department_str Computer Science{{{_:::_}}}College of Science{{{_:::_}}}Computer Science
document_store_str 1
active_str 1
researchgroup_str Visual Computing
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 2019-01-01T06:27:05Z
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score 10.836305