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

Mohammed Ali, Ali Alqahtani, Mark W. Jones and Xianghua Xie

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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.

Deunyddiau Ffynhonnell

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DOI

10.1109/ACCESS.2019.2958551
https://dx.doi.org/10.1109/ACCESS.2019.2958551

Enwi

Mohammed Ali, Ali Alqahtani, Mark W. Jones and Xianghua Xie, Clustering and Classification for Time Series Data in Visual Analytics: A Survey, IEEE Access 7(1), 181314-181338, December 2019. https://doi.org/10.1109/ACCESS.2019.2958551

Bibtex

@article{TimeSeriesSurvey2019, 
author={M. {Ali} and A. {Alqahtani} and M. W. {Jones} and X. {Xie}}, 
journal={IEEE Access}, 
title={Clustering and Classification for Time Series Data in Visual Analytics: A Survey}, 
year={2019}, 
volume={7}, 
number={1},
month={December},
pages={181314-181338}, 
keywords={Time series data;clustering;classification;visualization;visual analytics}, 
doi={10.1109/ACCESS.2019.2958551}, 
ISSN={2169-3536}, 
month={},}