Multivariate Time Series Density Clustering Algorithm Using Shapelet Space
Multivariate time series clustering has become an important research topic in the task of time series analysis. Compared with univariate time series, the research of multivariate time series is more complex and difficult. Although many clustering algorithms for multivariate time series have been pro...
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| Format: | Article |
| Language: | zho |
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Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press
2024-02-01
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| Series: | Jisuanji kexue yu tansuo |
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| Online Access: | http://fcst.ceaj.org/fileup/1673-9418/PDF/2211099.pdf |
| _version_ | 1797334202977353728 |
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| author | SHENG Jinchao, DU Mingjing, SUN Jiarui, LI Yurui |
| author_facet | SHENG Jinchao, DU Mingjing, SUN Jiarui, LI Yurui |
| author_sort | SHENG Jinchao, DU Mingjing, SUN Jiarui, LI Yurui |
| collection | DOAJ |
| description | Multivariate time series clustering has become an important research topic in the task of time series analysis. Compared with univariate time series, the research of multivariate time series is more complex and difficult. Although many clustering algorithms for multivariate time series have been proposed, these algorithms still have difficulties in solving the accuracy and interpretation at the same time. Firstly, most of the current work does not consider the length redundancy and variable correlation of multivariable time series, resulting in large errors in the final similarity matrix. Secondly, the data are commonly used in the clustering process with the division paradigm, when the numerical space presents a complex distribution, this idea does not perform well, and it does not have the explanatory power of each variable and space. To address the above problems, this paper proposes a multivariate time series adaptive weight density clustering algorithm using Shapelet (high information-rich continuous subsequence) space (MDCS). This algorithm firstly performs a Shapelet search for each variable, and obtains its own Shapelet space through an adaptive strategy. Then, it weights the numerical distribution generated by each variable to obtain a similarity matrix that is more consistent with the characteristics of data distribution. Finally, the data are finally allocated using the shared nearest neighbor density peak clustering algorithm with improved density calculation and secondary allocation. Experimental results on several real datasets demonstrate that MDCS has better clustering results compared with current state-of-the-art clustering algorithms, with an average increase of 0.344 and 0.09 in the normalized mutual information and Rand index, balancing performance and interpretability. |
| first_indexed | 2024-03-08T08:17:20Z |
| format | Article |
| id | doaj.art-626e8c4337e8480e9c14f9be3d26168f |
| institution | Directory Open Access Journal |
| issn | 1673-9418 |
| language | zho |
| last_indexed | 2024-03-08T08:17:20Z |
| publishDate | 2024-02-01 |
| publisher | Journal of Computer Engineering and Applications Beijing Co., Ltd., Science Press |
| record_format | Article |
| series | Jisuanji kexue yu tansuo |
| spelling | doaj.art-626e8c4337e8480e9c14f9be3d26168f2024-02-02T07:10:08ZzhoJournal of Computer Engineering and Applications Beijing Co., Ltd., Science PressJisuanji kexue yu tansuo1673-94182024-02-0118238740210.3778/j.issn.1673-9418.2211099Multivariate Time Series Density Clustering Algorithm Using Shapelet SpaceSHENG Jinchao, DU Mingjing, SUN Jiarui, LI Yurui0School of Computer Science and Technology, Jiangsu Normal University, Xuzhou, Jiangsu 221100, ChinaMultivariate time series clustering has become an important research topic in the task of time series analysis. Compared with univariate time series, the research of multivariate time series is more complex and difficult. Although many clustering algorithms for multivariate time series have been proposed, these algorithms still have difficulties in solving the accuracy and interpretation at the same time. Firstly, most of the current work does not consider the length redundancy and variable correlation of multivariable time series, resulting in large errors in the final similarity matrix. Secondly, the data are commonly used in the clustering process with the division paradigm, when the numerical space presents a complex distribution, this idea does not perform well, and it does not have the explanatory power of each variable and space. To address the above problems, this paper proposes a multivariate time series adaptive weight density clustering algorithm using Shapelet (high information-rich continuous subsequence) space (MDCS). This algorithm firstly performs a Shapelet search for each variable, and obtains its own Shapelet space through an adaptive strategy. Then, it weights the numerical distribution generated by each variable to obtain a similarity matrix that is more consistent with the characteristics of data distribution. Finally, the data are finally allocated using the shared nearest neighbor density peak clustering algorithm with improved density calculation and secondary allocation. Experimental results on several real datasets demonstrate that MDCS has better clustering results compared with current state-of-the-art clustering algorithms, with an average increase of 0.344 and 0.09 in the normalized mutual information and Rand index, balancing performance and interpretability.http://fcst.ceaj.org/fileup/1673-9418/PDF/2211099.pdfmultivariate time series; subseries; shapelet space; density peak clustering; data mining |
| spellingShingle | SHENG Jinchao, DU Mingjing, SUN Jiarui, LI Yurui Multivariate Time Series Density Clustering Algorithm Using Shapelet Space Jisuanji kexue yu tansuo multivariate time series; subseries; shapelet space; density peak clustering; data mining |
| title | Multivariate Time Series Density Clustering Algorithm Using Shapelet Space |
| title_full | Multivariate Time Series Density Clustering Algorithm Using Shapelet Space |
| title_fullStr | Multivariate Time Series Density Clustering Algorithm Using Shapelet Space |
| title_full_unstemmed | Multivariate Time Series Density Clustering Algorithm Using Shapelet Space |
| title_short | Multivariate Time Series Density Clustering Algorithm Using Shapelet Space |
| title_sort | multivariate time series density clustering algorithm using shapelet space |
| topic | multivariate time series; subseries; shapelet space; density peak clustering; data mining |
| url | http://fcst.ceaj.org/fileup/1673-9418/PDF/2211099.pdf |
| work_keys_str_mv | AT shengjinchaodumingjingsunjiaruiliyurui multivariatetimeseriesdensityclusteringalgorithmusingshapeletspace |