Clustering and Classification for Time Series Data in Visual Analytics: A Survey
Clustering and Classification for Time Series Data in Visual Analytics: A Survey
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DOI：
10.1109/access.2019.2958551
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Xie, Xianghua
中科院分区：
文献类型：
--
作者：
Ali, Mohammed;Alqahtani, Ali;Xie, Xianghua
时间序列数据的可视化分析受到了相当多的关注。人们开发了不同的方法来了解数据的特征并获取有意义的统计数据，以便探索潜在过程、识别和估计趋势、做出决策和预测未来。机器学习和可视化领域都专注于从数据中提取信息。在本文中，我们不仅考虑自动的方法，但也互动的探索。在交互式可视化系统中嵌入有效的机器学习技术（聚类和分类）的能力是非常可取的，以便从人类和计算机中获得最大的收益。我们提出了一些最重要的出版物在该领域的文献综述，并从六个不同的角度分类超过60发表的论文。本文旨在阐明聚类或分类算法用于时间序列数据的可视化分析的主要概念，并为新兴领域的研究人员和专家将机器学习技术集成到可视化分析中提供有价值的指导。
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.