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
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
收藏
页数:29
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