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Clustering and Classification for Time Series Data in Visual Analytics: A Survey

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

IEEE Access, Volume: 7, Pages: 181314 - 181338

Swansea University Authors: Mohammed Ali, Ali Alqahtani, Mark Jones Orcid Logo, Xianghua Xie Orcid Logo

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DOI (Published version): 10.1109/access.2019.2958551

Abstract

Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions an...

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Published in: IEEE Access
ISSN: 2169-3536
Published: Institute of Electrical and Electronics Engineers (IEEE) 2019
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URI: https://cronfa.swan.ac.uk/Record/cronfa52963
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first_indexed 2019-12-05T13:16:13Z
last_indexed 2020-09-03T03:17:16Z
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spelling 2020-09-02T17:08:26.3349933 v2 52963 2019-12-05 Clustering and Classification for Time Series Data in Visual Analytics: A Survey 192964f28b9898709d15e1ba9682a2f5 Mohammed Ali Mohammed Ali true false c0c682a8d9d12520f9639b89f9500946 Ali Alqahtani Ali Alqahtani true false 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2019-12-05 SCS Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics. Journal Article IEEE Access 7 181314 181338 Institute of Electrical and Electronics Engineers (IEEE) 2169-3536 time series data, clustering, classification, visualization, visual analytics 23 12 2019 2019-12-23 10.1109/access.2019.2958551 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University UKRI, EPSRC EP/N028139/1) 2020-09-02T17:08:26.3349933 2019-12-05T09:58:16.6722886 Mohammed Ali 1 Ali Alqahtani 2 Mark Jones 0000-0001-8991-1190 3 Xianghua Xie 0000-0002-2701-8660 4 52963__16243__dfaac47d5eaf4c47981f50fc361f46ab.pdf 52963.pdf 2020-01-08T14:25:13.5329563 Output 2258081 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution 4.0 License (CC-BY). true eng http://creativecommons.org/licenses/by/4.0/
title Clustering and Classification for Time Series Data in Visual Analytics: A Survey
spellingShingle Clustering and Classification for Time Series Data in Visual Analytics: A Survey
Mohammed Ali
Ali Alqahtani
Mark Jones
Xianghua Xie
title_short Clustering and Classification for Time Series Data in Visual Analytics: A Survey
title_full Clustering and Classification for Time Series Data in Visual Analytics: A Survey
title_fullStr Clustering and Classification for Time Series Data in Visual Analytics: A Survey
title_full_unstemmed Clustering and Classification for Time Series Data in Visual Analytics: A Survey
title_sort Clustering and Classification for Time Series Data in Visual Analytics: A Survey
author_id_str_mv 192964f28b9898709d15e1ba9682a2f5
c0c682a8d9d12520f9639b89f9500946
2e1030b6e14fc9debd5d5ae7cc335562
b334d40963c7a2f435f06d2c26c74e11
author_id_fullname_str_mv 192964f28b9898709d15e1ba9682a2f5_***_Mohammed Ali
c0c682a8d9d12520f9639b89f9500946_***_Ali Alqahtani
2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
author Mohammed Ali
Ali Alqahtani
Mark Jones
Xianghua Xie
author2 Mohammed Ali
Ali Alqahtani
Mark Jones
Xianghua Xie
format Journal article
container_title IEEE Access
container_volume 7
container_start_page 181314
publishDate 2019
institution Swansea University
issn 2169-3536
doi_str_mv 10.1109/access.2019.2958551
publisher Institute of Electrical and Electronics Engineers (IEEE)
document_store_str 1
active_str 0
description Visual analytics for time series data has received a considerable amount of attention. Different approaches have been developed to understand the characteristics of the data and obtain meaningful statistics in order to explore the underlying processes, identify and estimate trends, make decisions and predict the future. The machine learning and visualization areas share a focus on extracting information from data. In this paper, we consider not only automatic methods but also interactive exploration. The ability to embed efficient machine learning techniques (clustering and classification) in interactive visualization systems is highly desirable in order to gain the most from both humans and computers. We present a literature review of some of the most important publications in the field and classify over 60 published papers from six different perspectives. This review intends to clarify the major concepts with which clustering or classification algorithms are used in visual analytics for time series data and provide a valuable guide for both new researchers and experts in the emerging field of integrating machine learning techniques into visual analytics.
published_date 2019-12-23T04:05:39Z
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score 11.013037

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