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Deep Time-Series Clustering: A Review

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

Electronics, Volume: 10, Issue: 23, Start page: 3001

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

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Abstract

We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series da...

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Published in: Electronics
ISSN: 2079-9292
Published: MDPI AG 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa58874
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first_indexed 2021-12-02T10:12:17Z
last_indexed 2022-01-01T04:25:16Z
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spelling 2021-12-31T13:37:21.5641741 v2 58874 2021-12-02 Deep Time-Series Clustering: A Review c0c682a8d9d12520f9639b89f9500946 Ali Alqahtani Ali Alqahtani true false 192964f28b9898709d15e1ba9682a2f5 Mohammed Ali Mohammed Ali true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false 2e1030b6e14fc9debd5d5ae7cc335562 0000-0001-8991-1190 Mark Jones Mark Jones true false 2021-12-02 SCS We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives. Journal Article Electronics 10 23 3001 MDPI AG 2079-9292 deep learning; clustering; time series data 2 12 2021 2021-12-02 10.3390/electronics10233001 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University Another institution paid the OA fee Deanship of Scientific Research, King Khalid University of Kingdom of Saudi Arabia under research grant number (RGP1/207/42). 2021-12-31T13:37:21.5641741 2021-12-02T10:09:56.0146654 College of Science Computer Science Ali Alqahtani 1 Mohammed Ali 2 Xianghua Xie 0000-0002-2701-8660 3 Mark Jones 0000-0001-8991-1190 4 58874__21768__b4934e3d147745f6938d5b6d6ea0b57c.pdf electronics-10-03001.pdf 2021-12-02T10:11:10.0369949 Output 1852521 application/pdf Version of Record true © 2021 by the authors. This is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license true eng https://creativecommons.org/licenses/by/4.0/
title Deep Time-Series Clustering: A Review
spellingShingle Deep Time-Series Clustering: A Review
Ali Alqahtani
Mohammed Ali
Xianghua Xie
Mark Jones
title_short Deep Time-Series Clustering: A Review
title_full Deep Time-Series Clustering: A Review
title_fullStr Deep Time-Series Clustering: A Review
title_full_unstemmed Deep Time-Series Clustering: A Review
title_sort Deep Time-Series Clustering: A Review
author_id_str_mv c0c682a8d9d12520f9639b89f9500946
192964f28b9898709d15e1ba9682a2f5
b334d40963c7a2f435f06d2c26c74e11
2e1030b6e14fc9debd5d5ae7cc335562
author_id_fullname_str_mv c0c682a8d9d12520f9639b89f9500946_***_Ali Alqahtani
192964f28b9898709d15e1ba9682a2f5_***_Mohammed Ali
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
2e1030b6e14fc9debd5d5ae7cc335562_***_Mark Jones
author Ali Alqahtani
Mohammed Ali
Xianghua Xie
Mark Jones
author2 Ali Alqahtani
Mohammed Ali
Xianghua Xie
Mark Jones
format Journal article
container_title Electronics
container_volume 10
container_issue 23
container_start_page 3001
publishDate 2021
institution Swansea University
issn 2079-9292
doi_str_mv 10.3390/electronics10233001
publisher MDPI AG
college_str College of Science
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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
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description We present a comprehensive, detailed review of time-series data analysis, with emphasis on deep time-series clustering (DTSC), and a case study in the context of movement behavior clustering utilizing the deep clustering method. Specifically, we modified the DCAE architectures to suit time-series data at the time of our prior deep clustering work. Lately, several works have been carried out on deep clustering of time-series data. We also review these works and identify state-of-the-art, as well as present an outlook on this important field of DTSC from five important perspectives.
published_date 2021-12-02T04:15:48Z
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score 10.878401