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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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Detalles Bibliográficos
Autores principales: Zolhavarieh, Seyedjamal, Aghabozorgi, Saeed, Teh, Ying Wah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4130317/
https://www.ncbi.nlm.nih.gov/pubmed/25140332
http://dx.doi.org/10.1155/2014/312521
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author Zolhavarieh, Seyedjamal
Aghabozorgi, Saeed
Teh, Ying Wah
author_facet Zolhavarieh, Seyedjamal
Aghabozorgi, Saeed
Teh, Ying Wah
author_sort Zolhavarieh, Seyedjamal
collection PubMed
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.
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spelling pubmed-41303172014-08-19 A Review of Subsequence Time Series Clustering Zolhavarieh, Seyedjamal Aghabozorgi, Saeed Teh, Ying Wah ScientificWorldJournal Review Article 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. Hindawi Publishing Corporation 2014 2014-07-21 /pmc/articles/PMC4130317/ /pubmed/25140332 http://dx.doi.org/10.1155/2014/312521 Text en Copyright © 2014 Seyedjamal Zolhavarieh et al. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Review Article
Zolhavarieh, Seyedjamal
Aghabozorgi, Saeed
Teh, Ying Wah
A Review of Subsequence Time Series Clustering
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
topic Review Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4130317/
https://www.ncbi.nlm.nih.gov/pubmed/25140332
http://dx.doi.org/10.1155/2014/312521
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