Clustering and Classification for Time Series Data in Visual Analytics: A Survey
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 an...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8930535/ |
| _version_ | 1819133502753341440 |
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| author | Mohammed Ali Ali Alqahtani Mark W. Jones Xianghua Xie |
| author_facet | Mohammed Ali Ali Alqahtani Mark W. Jones Xianghua Xie |
| author_sort | Mohammed Ali |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-22T09:48:19Z |
| format | Article |
| id | doaj.art-04d1188bd60642a296e97bd5e0d33e54 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-22T09:48:19Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-04d1188bd60642a296e97bd5e0d33e542022-12-21T18:30:27ZengIEEEIEEE Access2169-35362019-01-01718131418133810.1109/ACCESS.2019.29585518930535Clustering and Classification for Time Series Data in Visual Analytics: A SurveyMohammed Ali0https://orcid.org/0000-0002-5908-4013Ali Alqahtani1https://orcid.org/0000-0003-1052-2657Mark W. Jones2https://orcid.org/0000-0001-8991-1190Xianghua Xie3https://orcid.org/0000-0002-2701-8660Department of Computer Science, Swansea University, Swansea, U.K.Department of Computer Science, Swansea University, Swansea, U.K.Department of Computer Science, Swansea University, Swansea, U.K.Department of Computer Science, Swansea University, Swansea, U.K.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.https://ieeexplore.ieee.org/document/8930535/Time series dataclusteringclassificationvisualizationvisual analytics |
| spellingShingle | Mohammed Ali Ali Alqahtani Mark W. Jones Xianghua Xie Clustering and Classification for Time Series Data in Visual Analytics: A Survey IEEE Access Time series data clustering classification visualization visual analytics |
| title | Clustering and Classification for Time Series Data in Visual Analytics: A Survey |
| title_full | Clustering and Classification for Time Series Data in Visual Analytics: A Survey |
| title_fullStr | Clustering and Classification for Time Series Data in Visual Analytics: A Survey |
| title_full_unstemmed | Clustering and Classification for Time Series Data in Visual Analytics: A Survey |
| title_short | Clustering and Classification for Time Series Data in Visual Analytics: A Survey |
| title_sort | clustering and classification for time series data in visual analytics a survey |
| topic | Time series data clustering classification visualization visual analytics |
| url | https://ieeexplore.ieee.org/document/8930535/ |
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