Time series data intelligent clustering algorithm for landslide displacement prediction

The traditional time series data clustering for landslide displacement prediction is based on Euclidean distance measure. The time series data is clustered by distance calculation of two vectors. The correlation between components is not considered. The multiple components with single feature will i...

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Main Authors:	Han, Liu, Shang, Tao, Shu, Jisen, Khan Chowdhury, Ahmed Jalal
Format:	Article
Language:	English English English
Published:	IOS PRESS 2018
Subjects:	Q Science (General) QA276 Mathematical Statistics
Online Access:	http://irep.iium.edu.my/68305/ http://irep.iium.edu.my/68305/ http://irep.iium.edu.my/68305/ http://irep.iium.edu.my/68305/19/68305_Time%20series%20data%20intelligent%20clustering%20algorithm_article.pdf http://irep.iium.edu.my/68305/7/68305_Time%20series%20data%20intelligent%20clustering_SCOPUS.pdf http://irep.iium.edu.my/68305/8/68305_Time%20series%20data%20intelligent%20clustering_WOS.pdf

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spelling	iium-683052019-09-12T05:18:50Z http://irep.iium.edu.my/68305/ Time series data intelligent clustering algorithm for landslide displacement prediction Han, Liu Shang, Tao Shu, Jisen Khan Chowdhury, Ahmed Jalal Q Science (General) QA276 Mathematical Statistics The traditional time series data clustering for landslide displacement prediction is based on Euclidean distance measure. The time series data is clustered by distance calculation of two vectors. The correlation between components is not considered. The multiple components with single feature will interfere with the clustering results, and the accuracy of clustering results is greatly reduced. To address this problem, an intelligent clustering algorithm for time series data in landslide displacement prediction based on nonlinear dynamic time bending is proposed in this paper. By reconstructing the phase space of the landslide displacement time series, the phase space transposed matrix is obtained as the time series reconstruction matrix. After embedding dimension processing, the time series of landslide displacement is predicted by SVM data mining model. Dynamic time warping calculation is based on the correlation of time series sequence and the components. The local optimal solution is obtained by recursive search, and the whole curve path is obtained. Clustering calculation of time series data set is carried out by using hierarchical clustering algorithm according to bending path. The intelligent clustering results of time series data in landslide displacement prediction is obtained. Experimental results show that the proposed algorithm has better clustering effect and higher clustering accuracy. IOS PRESS 2018-10 Article PeerReviewed application/pdf en http://irep.iium.edu.my/68305/19/68305_Time%20series%20data%20intelligent%20clustering%20algorithm_article.pdf application/pdf en http://irep.iium.edu.my/68305/7/68305_Time%20series%20data%20intelligent%20clustering_SCOPUS.pdf application/pdf en http://irep.iium.edu.my/68305/8/68305_Time%20series%20data%20intelligent%20clustering_WOS.pdf Han, Liu and Shang, Tao and Shu, Jisen and Khan Chowdhury, Ahmed Jalal (2018) Time series data intelligent clustering algorithm for landslide displacement prediction. Journal of Intelligent & Fuzzy Systems, 35 (4). pp. 4131-4140. ISSN 1064-1246 E-ISSN 1875-8967 https://content.iospress.com/articles/journal-of-intelligent-and-fuzzy-systems/ifs169734 10.3233/JIFS-169734
repository_type	Digital Repository
institution_category	Local University
institution	International Islamic University Malaysia
building	IIUM Repository
collection	Online Access
language	English English English
topic	Q Science (General) QA276 Mathematical Statistics
spellingShingle	Q Science (General) QA276 Mathematical Statistics Han, Liu Shang, Tao Shu, Jisen Khan Chowdhury, Ahmed Jalal Time series data intelligent clustering algorithm for landslide displacement prediction
description	The traditional time series data clustering for landslide displacement prediction is based on Euclidean distance measure. The time series data is clustered by distance calculation of two vectors. The correlation between components is not considered. The multiple components with single feature will interfere with the clustering results, and the accuracy of clustering results is greatly reduced. To address this problem, an intelligent clustering algorithm for time series data in landslide displacement prediction based on nonlinear dynamic time bending is proposed in this paper. By reconstructing the phase space of the landslide displacement time series, the phase space transposed matrix is obtained as the time series reconstruction matrix. After embedding dimension processing, the time series of landslide displacement is predicted by SVM data mining model. Dynamic time warping calculation is based on the correlation of time series sequence and the components. The local optimal solution is obtained by recursive search, and the whole curve path is obtained. Clustering calculation of time series data set is carried out by using hierarchical clustering algorithm according to bending path. The intelligent clustering results of time series data in landslide displacement prediction is obtained. Experimental results show that the proposed algorithm has better clustering effect and higher clustering accuracy.
format	Article
author	Han, Liu Shang, Tao Shu, Jisen Khan Chowdhury, Ahmed Jalal
author_facet	Han, Liu Shang, Tao Shu, Jisen Khan Chowdhury, Ahmed Jalal
author_sort	Han, Liu
title	Time series data intelligent clustering algorithm for landslide displacement prediction
title_short	Time series data intelligent clustering algorithm for landslide displacement prediction
title_full	Time series data intelligent clustering algorithm for landslide displacement prediction
title_fullStr	Time series data intelligent clustering algorithm for landslide displacement prediction
title_full_unstemmed	Time series data intelligent clustering algorithm for landslide displacement prediction
title_sort	time series data intelligent clustering algorithm for landslide displacement prediction
publisher	IOS PRESS
publishDate	2018
url	http://irep.iium.edu.my/68305/ http://irep.iium.edu.my/68305/ http://irep.iium.edu.my/68305/ http://irep.iium.edu.my/68305/19/68305_Time%20series%20data%20intelligent%20clustering%20algorithm_article.pdf http://irep.iium.edu.my/68305/7/68305_Time%20series%20data%20intelligent%20clustering_SCOPUS.pdf http://irep.iium.edu.my/68305/8/68305_Time%20series%20data%20intelligent%20clustering_WOS.pdf
first_indexed	2023-09-18T21:36:56Z
last_indexed	2023-09-18T21:36:56Z
_version_	1777412883312803840

Time series data intelligent clustering algorithm for landslide displacement prediction

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