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Deep Time-Series Clustering: A Review
,
Mark Jones
Electronics, Volume: 10, Issue: 23, Start page: 3001
Swansea University Authors:
Ali Alqahtani, Mohammed Ali, Xianghua Xie , Mark Jones
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DOI (Published version): 10.3390/electronics10233001
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...
| Published in: | Electronics |
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| ISSN: | 2079-9292 |
| Published: |
MDPI AG
2021
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| Online Access: |
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa58874 |
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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 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. |
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| Keywords: |
deep learning; clustering; time series data |
| College: |
Faculty of Science and Engineering |
| Funders: |
Deanship of Scientific Research, King Khalid University of Kingdom of Saudi Arabia under research grant number (RGP1/207/42). |
| Issue: |
23 |
| Start Page: |
3001 |