Deep Time-Series Clustering: A Review

被引：28

作者：

Alqahtani, Ali ^{[1
,2
,3
]}

Ali, Mohammed ^{[2
,3
]}

Xie, Xianghua ^{[3
]}

Jones, Mark W. ^{[3
]}

机构：

[1] King Khalid Univ, Ctr Artificial Intelligence CAI, Abha 61421, Saudi Arabia

[2] King Khalid Univ, Dept Comp Sci, Abha 61421, Saudi Arabia

[3] Swansea Univ, Dept Comp Sci, Swansea SA1 8EN, England

来源：

ELECTRONICS | 2021年 / 10卷 / 23期

关键词：

deep learning; clustering; time series data; VISUAL ANALYTICS APPROACH; SELF-ORGANIZING MAPS; ANOMALY DETECTION; CLASSIFICATION; VISUALIZATION; SIMILARITY; NETWORK; SEARCH; SYSTEM; OPTIMIZATION;

D O I：

10.3390/electronics10233001

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页数：29

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