A Review of Subsequence Time Series Clustering
Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the doma...
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2014/312521 |
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| author | Seyedjamal Zolhavarieh Saeed Aghabozorgi Ying Wah Teh |
| author_facet | Seyedjamal Zolhavarieh Saeed Aghabozorgi Ying Wah Teh |
| author_sort | Seyedjamal Zolhavarieh |
| collection | DOAJ |
| description | Clustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies. |
| format | Article |
| id | doaj-art-4eec1a8b604e4d2ba42e58ced83b1c4f |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-4eec1a8b604e4d2ba42e58ced83b1c4f2025-02-03T05:51:08ZengWileyThe Scientific World Journal2356-61401537-744X2014-01-01201410.1155/2014/312521312521A Review of Subsequence Time Series ClusteringSeyedjamal Zolhavarieh0Saeed Aghabozorgi1Ying Wah Teh2Department of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya (UM), 50603 Kuala Lumpur, MalaysiaDepartment of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya (UM), 50603 Kuala Lumpur, MalaysiaDepartment of Information Systems, Faculty of Computer Science and Information Technology, University of Malaya (UM), 50603 Kuala Lumpur, MalaysiaClustering of subsequence time series remains an open issue in time series clustering. Subsequence time series clustering is used in different fields, such as e-commerce, outlier detection, speech recognition, biological systems, DNA recognition, and text mining. One of the useful fields in the domain of subsequence time series clustering is pattern recognition. To improve this field, a sequence of time series data is used. This paper reviews some definitions and backgrounds related to subsequence time series clustering. The categorization of the literature reviews is divided into three groups: preproof, interproof, and postproof period. Moreover, various state-of-the-art approaches in performing subsequence time series clustering are discussed under each of the following categories. The strengths and weaknesses of the employed methods are evaluated as potential issues for future studies.http://dx.doi.org/10.1155/2014/312521 |
| spellingShingle | Seyedjamal Zolhavarieh Saeed Aghabozorgi Ying Wah Teh A Review of Subsequence Time Series Clustering The Scientific World Journal |
| title | A Review of Subsequence Time Series Clustering |
| title_full | A Review of Subsequence Time Series Clustering |
| title_fullStr | A Review of Subsequence Time Series Clustering |
| title_full_unstemmed | A Review of Subsequence Time Series Clustering |
| title_short | A Review of Subsequence Time Series Clustering |
| title_sort | review of subsequence time series clustering |
| url | http://dx.doi.org/10.1155/2014/312521 |
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