A Hybrid Approach for Clustering Uncertain Time Series

Information uncertainty extensively exists in the real-world applications, and uncertain data process and analysis have been a crucial issue in the area of data and knowledge engineering. In this paper, we concentrate on uncertain time series data clustering, in which the uncertain values at time po...

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Autores principales:	Ruizhe Ma, Xiaoping Zhu, Li Yan
Formato:	article
Lenguaje:	EN
Publicado:	University of Zagreb Faculty of Electrical Engineering and Computing 2020
Materias:	Uncertain time series UK-Means clustering DTW with limited width Hierarchical clustering ARI Electronic computers. Computer science QA75.5-76.95
Acceso en línea:	https://doaj.org/article/0b14e783d4ba4481aed58f4987e5ab55
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id	oai:doaj.org-article:0b14e783d4ba4481aed58f4987e5ab55
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spelling	oai:doaj.org-article:0b14e783d4ba4481aed58f4987e5ab552021-12-02T17:55:41ZA Hybrid Approach for Clustering Uncertain Time Series1330-11361846-3908https://doaj.org/article/0b14e783d4ba4481aed58f4987e5ab552020-01-01T00:00:00Zhttps://hrcak.srce.hr/file/384992https://doaj.org/toc/1330-1136https://doaj.org/toc/1846-3908Information uncertainty extensively exists in the real-world applications, and uncertain data process and analysis have been a crucial issue in the area of data and knowledge engineering. In this paper, we concentrate on uncertain time series data clustering, in which the uncertain values at time points are represented by probability density function. We propose a hybrid clustering approach for uncertain time series. Our clustering approach first partitions the uncertain time series data into a set of micro-clusters and then merges the micro-clusters following the idea of hierarchical clustering. We evaluate our approach with experiments. The experimental results show that, compared with the traditional UK-means clustering algorithm, the Adjusted Rand Index (ARI) of our clustering results have an obviously higher accuracy. In addition, the time efficiency of our clustering approach is significantly improved.Ruizhe MaXiaoping ZhuLi YanUniversity of Zagreb Faculty of Electrical Engineering and ComputingarticleUncertain time seriesUK-Means clusteringDTW with limited widthHierarchical clusteringARIElectronic computers. Computer scienceQA75.5-76.95ENJournal of Computing and Information Technology, Vol 28, Iss 4, Pp 255-267 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Uncertain time series UK-Means clustering DTW with limited width Hierarchical clustering ARI Electronic computers. Computer science QA75.5-76.95
spellingShingle	Uncertain time series UK-Means clustering DTW with limited width Hierarchical clustering ARI Electronic computers. Computer science QA75.5-76.95 Ruizhe Ma Xiaoping Zhu Li Yan A Hybrid Approach for Clustering Uncertain Time Series
description	Information uncertainty extensively exists in the real-world applications, and uncertain data process and analysis have been a crucial issue in the area of data and knowledge engineering. In this paper, we concentrate on uncertain time series data clustering, in which the uncertain values at time points are represented by probability density function. We propose a hybrid clustering approach for uncertain time series. Our clustering approach first partitions the uncertain time series data into a set of micro-clusters and then merges the micro-clusters following the idea of hierarchical clustering. We evaluate our approach with experiments. The experimental results show that, compared with the traditional UK-means clustering algorithm, the Adjusted Rand Index (ARI) of our clustering results have an obviously higher accuracy. In addition, the time efficiency of our clustering approach is significantly improved.
format	article
author	Ruizhe Ma Xiaoping Zhu Li Yan
author_facet	Ruizhe Ma Xiaoping Zhu Li Yan
author_sort	Ruizhe Ma
title	A Hybrid Approach for Clustering Uncertain Time Series
title_short	A Hybrid Approach for Clustering Uncertain Time Series
title_full	A Hybrid Approach for Clustering Uncertain Time Series
title_fullStr	A Hybrid Approach for Clustering Uncertain Time Series
title_full_unstemmed	A Hybrid Approach for Clustering Uncertain Time Series
title_sort	hybrid approach for clustering uncertain time series
publisher	University of Zagreb Faculty of Electrical Engineering and Computing
publishDate	2020
url	https://doaj.org/article/0b14e783d4ba4481aed58f4987e5ab55
work_keys_str_mv	AT ruizhema ahybridapproachforclusteringuncertaintimeseries AT xiaopingzhu ahybridapproachforclusteringuncertaintimeseries AT liyan ahybridapproachforclusteringuncertaintimeseries AT ruizhema hybridapproachforclusteringuncertaintimeseries AT xiaopingzhu hybridapproachforclusteringuncertaintimeseries AT liyan hybridapproachforclusteringuncertaintimeseries
_version_	1718379087225946112

A Hybrid Approach for Clustering Uncertain Time Series

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