Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform
Abstract This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along the data with a pen of a given thickness. Since TPT is a multi-scale visualization technique,...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kim, Minji [verfasserIn] |
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| Format: |
Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2023 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2023 |
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| Übergeordnetes Werk: |
Enthalten in: Journal of classification - Springer US, 1984, 40(2023), 2 vom: 12. Juni, Seite 407-431 |
|---|---|
| Übergeordnetes Werk: |
volume:40 ; year:2023 ; number:2 ; day:12 ; month:06 ; pages:407-431 |
| Links: |
|---|
| DOI / URN: |
10.1007/s00357-023-09437-z |
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| Katalog-ID: |
OLC2144675023 |
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| 520 | |a Abstract This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along the data with a pen of a given thickness. Since TPT is a multi-scale visualization technique, it provides some information on the temporal tendency of neighborhood values. We introduce a modified TPT, termed ‘ensemble TPT (e-TPT)’, to enhance the temporal resolution of zero-inflated time series data that is crucial for clustering them efficiently. Furthermore, this study defines a modified similarity measure for zero-inflated time series data considering e-TPT and proposes an efficient iterative clustering algorithm suitable for the proposed measure. Finally, the effectiveness of the proposed method is demonstrated by simulation experiments and two real datasets: step count data and newly confirmed COVID-19 case data. | ||
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10.1007/s00357-023-09437-z doi (DE-627)OLC2144675023 (DE-He213)s00357-023-09437-z-p DE-627 ger DE-627 rakwb eng 150 510 600 VZ 24,1 ssgn Kim, Minji verfasserin aut Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along the data with a pen of a given thickness. Since TPT is a multi-scale visualization technique, it provides some information on the temporal tendency of neighborhood values. We introduce a modified TPT, termed ‘ensemble TPT (e-TPT)’, to enhance the temporal resolution of zero-inflated time series data that is crucial for clustering them efficiently. Furthermore, this study defines a modified similarity measure for zero-inflated time series data considering e-TPT and proposes an efficient iterative clustering algorithm suitable for the proposed measure. Finally, the effectiveness of the proposed method is demonstrated by simulation experiments and two real datasets: step count data and newly confirmed COVID-19 case data. Clustering Multiscale method Newly confirmed COVID-19 case data Step count data Thick-pen transform Zero-inflated time series data Oh, Hee-Seok aut Lim, Yaeji (orcid)0000-0002-8698-8667 aut Enthalten in Journal of classification Springer US, 1984 40(2023), 2 vom: 12. Juni, Seite 407-431 (DE-627)129337323 (DE-600)142885-8 (DE-576)014642832 0176-4268 nnns volume:40 year:2023 number:2 day:12 month:06 pages:407-431 https://doi.org/10.1007/s00357-023-09437-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_2018 AR 40 2023 2 12 06 407-431 |
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10.1007/s00357-023-09437-z doi (DE-627)OLC2144675023 (DE-He213)s00357-023-09437-z-p DE-627 ger DE-627 rakwb eng 150 510 600 VZ 24,1 ssgn Kim, Minji verfasserin aut Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform 2023 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2023 Abstract This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along the data with a pen of a given thickness. Since TPT is a multi-scale visualization technique, it provides some information on the temporal tendency of neighborhood values. We introduce a modified TPT, termed ‘ensemble TPT (e-TPT)’, to enhance the temporal resolution of zero-inflated time series data that is crucial for clustering them efficiently. Furthermore, this study defines a modified similarity measure for zero-inflated time series data considering e-TPT and proposes an efficient iterative clustering algorithm suitable for the proposed measure. Finally, the effectiveness of the proposed method is demonstrated by simulation experiments and two real datasets: step count data and newly confirmed COVID-19 case data. Clustering Multiscale method Newly confirmed COVID-19 case data Step count data Thick-pen transform Zero-inflated time series data Oh, Hee-Seok aut Lim, Yaeji (orcid)0000-0002-8698-8667 aut Enthalten in Journal of classification Springer US, 1984 40(2023), 2 vom: 12. Juni, Seite 407-431 (DE-627)129337323 (DE-600)142885-8 (DE-576)014642832 0176-4268 nnns volume:40 year:2023 number:2 day:12 month:06 pages:407-431 https://doi.org/10.1007/s00357-023-09437-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-TEC SSG-OLC-PHY SSG-OLC-CHE SSG-OLC-MAT SSG-OLC-BUB SSG-OPC-BBI SSG-OPC-MAT GBV_ILN_2018 AR 40 2023 2 12 06 407-431 |
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Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform |
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Abstract This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along the data with a pen of a given thickness. Since TPT is a multi-scale visualization technique, it provides some information on the temporal tendency of neighborhood values. We introduce a modified TPT, termed ‘ensemble TPT (e-TPT)’, to enhance the temporal resolution of zero-inflated time series data that is crucial for clustering them efficiently. Furthermore, this study defines a modified similarity measure for zero-inflated time series data considering e-TPT and proposes an efficient iterative clustering algorithm suitable for the proposed measure. Finally, the effectiveness of the proposed method is demonstrated by simulation experiments and two real datasets: step count data and newly confirmed COVID-19 case data. © The Author(s) 2023 |
| abstractGer |
Abstract This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along the data with a pen of a given thickness. Since TPT is a multi-scale visualization technique, it provides some information on the temporal tendency of neighborhood values. We introduce a modified TPT, termed ‘ensemble TPT (e-TPT)’, to enhance the temporal resolution of zero-inflated time series data that is crucial for clustering them efficiently. Furthermore, this study defines a modified similarity measure for zero-inflated time series data considering e-TPT and proposes an efficient iterative clustering algorithm suitable for the proposed measure. Finally, the effectiveness of the proposed method is demonstrated by simulation experiments and two real datasets: step count data and newly confirmed COVID-19 case data. © The Author(s) 2023 |
| abstract_unstemmed |
Abstract This study develops a new clustering method for high-dimensional zero-inflated time series data. The proposed method is based on thick-pen transform (TPT), in which the basic idea is to draw along the data with a pen of a given thickness. Since TPT is a multi-scale visualization technique, it provides some information on the temporal tendency of neighborhood values. We introduce a modified TPT, termed ‘ensemble TPT (e-TPT)’, to enhance the temporal resolution of zero-inflated time series data that is crucial for clustering them efficiently. Furthermore, this study defines a modified similarity measure for zero-inflated time series data considering e-TPT and proposes an efficient iterative clustering algorithm suitable for the proposed measure. Finally, the effectiveness of the proposed method is demonstrated by simulation experiments and two real datasets: step count data and newly confirmed COVID-19 case data. © The Author(s) 2023 |
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| container_issue |
2 |
| title_short |
Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform |
| url |
https://doi.org/10.1007/s00357-023-09437-z |
| remote_bool |
false |
| author2 |
Oh, Hee-Seok Lim, Yaeji |
| author2Str |
Oh, Hee-Seok Lim, Yaeji |
| ppnlink |
129337323 |
| mediatype_str_mv |
n |
| isOA_txt |
false |
| hochschulschrift_bool |
false |
| doi_str |
10.1007/s00357-023-09437-z |
| up_date |
2024-07-03T23:45:13.443Z |
| _version_ |
1803603477476147201 |
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7.4002113 |