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
Autor*in: |
Kim, Minji [verfasserIn] |
---|
Format: |
Artikel |
---|---|
Sprache: |
Englisch |
Erschienen: |
2023 |
---|
Schlagwörter: |
---|
Anmerkung: |
© The Author(s) 2023 |
---|
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 |
---|
Katalog-ID: |
OLC2144675023 |
---|
LEADER | 01000naa a22002652 4500 | ||
---|---|---|---|
001 | OLC2144675023 | ||
003 | DE-627 | ||
005 | 20240118100327.0 | ||
007 | tu | ||
008 | 240118s2023 xx ||||| 00| ||eng c | ||
024 | 7 | |a 10.1007/s00357-023-09437-z |2 doi | |
035 | |a (DE-627)OLC2144675023 | ||
035 | |a (DE-He213)s00357-023-09437-z-p | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | 4 | |a 150 |a 510 |a 600 |q VZ |
084 | |a 24,1 |2 ssgn | ||
100 | 1 | |a Kim, Minji |e verfasserin |4 aut | |
245 | 1 | 0 | |a Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform |
264 | 1 | |c 2023 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a ohne Hilfsmittel zu benutzen |b n |2 rdamedia | ||
338 | |a Band |b nc |2 rdacarrier | ||
500 | |a © The Author(s) 2023 | ||
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. | ||
650 | 4 | |a Clustering | |
650 | 4 | |a Multiscale method | |
650 | 4 | |a Newly confirmed COVID-19 case data | |
650 | 4 | |a Step count data | |
650 | 4 | |a Thick-pen transform | |
650 | 4 | |a Zero-inflated time series data | |
700 | 1 | |a Oh, Hee-Seok |4 aut | |
700 | 1 | |a Lim, Yaeji |0 (orcid)0000-0002-8698-8667 |4 aut | |
773 | 0 | 8 | |i Enthalten in |t Journal of classification |d Springer US, 1984 |g 40(2023), 2 vom: 12. Juni, Seite 407-431 |w (DE-627)129337323 |w (DE-600)142885-8 |w (DE-576)014642832 |x 0176-4268 |7 nnns |
773 | 1 | 8 | |g volume:40 |g year:2023 |g number:2 |g day:12 |g month:06 |g pages:407-431 |
856 | 4 | 1 | |u https://doi.org/10.1007/s00357-023-09437-z |z lizenzpflichtig |3 Volltext |
912 | |a GBV_USEFLAG_A | ||
912 | |a SYSFLAG_A | ||
912 | |a GBV_OLC | ||
912 | |a SSG-OLC-TEC | ||
912 | |a SSG-OLC-PHY | ||
912 | |a SSG-OLC-CHE | ||
912 | |a SSG-OLC-MAT | ||
912 | |a SSG-OLC-BUB | ||
912 | |a SSG-OPC-BBI | ||
912 | |a SSG-OPC-MAT | ||
912 | |a GBV_ILN_2018 | ||
951 | |a AR | ||
952 | |d 40 |j 2023 |e 2 |b 12 |c 06 |h 407-431 |
author_variant |
m k mk h s o hso y l yl |
---|---|
matchkey_str |
article:01764268:2023----::eonltdieeislseigiesmlt |
hierarchy_sort_str |
2023 |
publishDate |
2023 |
allfields |
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 |
spelling |
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 |
allfields_unstemmed |
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 |
allfieldsGer |
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 |
allfieldsSound |
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 |
language |
English |
source |
Enthalten in Journal of classification 40(2023), 2 vom: 12. Juni, Seite 407-431 volume:40 year:2023 number:2 day:12 month:06 pages:407-431 |
sourceStr |
Enthalten in Journal of classification 40(2023), 2 vom: 12. Juni, Seite 407-431 volume:40 year:2023 number:2 day:12 month:06 pages:407-431 |
format_phy_str_mv |
Article |
institution |
findex.gbv.de |
topic_facet |
Clustering Multiscale method Newly confirmed COVID-19 case data Step count data Thick-pen transform Zero-inflated time series data |
dewey-raw |
150 |
isfreeaccess_bool |
false |
container_title |
Journal of classification |
authorswithroles_txt_mv |
Kim, Minji @@aut@@ Oh, Hee-Seok @@aut@@ Lim, Yaeji @@aut@@ |
publishDateDaySort_date |
2023-06-12T00:00:00Z |
hierarchy_top_id |
129337323 |
dewey-sort |
3150 |
id |
OLC2144675023 |
language_de |
englisch |
fullrecord |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2144675023</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118100327.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00357-023-09437-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2144675023</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00357-023-09437-z-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="a">510</subfield><subfield code="a">600</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kim, Minji</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiscale method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Newly confirmed COVID-19 case data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Step count data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Thick-pen transform</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Zero-inflated time series data</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Oh, Hee-Seok</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lim, Yaeji</subfield><subfield code="0">(orcid)0000-0002-8698-8667</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of classification</subfield><subfield code="d">Springer US, 1984</subfield><subfield code="g">40(2023), 2 vom: 12. Juni, Seite 407-431</subfield><subfield code="w">(DE-627)129337323</subfield><subfield code="w">(DE-600)142885-8</subfield><subfield code="w">(DE-576)014642832</subfield><subfield code="x">0176-4268</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:40</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:2</subfield><subfield code="g">day:12</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:407-431</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00357-023-09437-z</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">40</subfield><subfield code="j">2023</subfield><subfield code="e">2</subfield><subfield code="b">12</subfield><subfield code="c">06</subfield><subfield code="h">407-431</subfield></datafield></record></collection>
|
author |
Kim, Minji |
spellingShingle |
Kim, Minji ddc 150 ssgn 24,1 misc Clustering misc Multiscale method misc Newly confirmed COVID-19 case data misc Step count data misc Thick-pen transform misc Zero-inflated time series data Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform |
authorStr |
Kim, Minji |
ppnlink_with_tag_str_mv |
@@773@@(DE-627)129337323 |
format |
Article |
dewey-ones |
150 - Psychology 510 - Mathematics 600 - Technology |
delete_txt_mv |
keep |
author_role |
aut aut aut |
collection |
OLC |
remote_str |
false |
illustrated |
Not Illustrated |
issn |
0176-4268 |
topic_title |
150 510 600 VZ 24,1 ssgn Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform Clustering Multiscale method Newly confirmed COVID-19 case data Step count data Thick-pen transform Zero-inflated time series data |
topic |
ddc 150 ssgn 24,1 misc Clustering misc Multiscale method misc Newly confirmed COVID-19 case data misc Step count data misc Thick-pen transform misc Zero-inflated time series data |
topic_unstemmed |
ddc 150 ssgn 24,1 misc Clustering misc Multiscale method misc Newly confirmed COVID-19 case data misc Step count data misc Thick-pen transform misc Zero-inflated time series data |
topic_browse |
ddc 150 ssgn 24,1 misc Clustering misc Multiscale method misc Newly confirmed COVID-19 case data misc Step count data misc Thick-pen transform misc Zero-inflated time series data |
format_facet |
Aufsätze Gedruckte Aufsätze |
format_main_str_mv |
Text Zeitschrift/Artikel |
carriertype_str_mv |
nc |
hierarchy_parent_title |
Journal of classification |
hierarchy_parent_id |
129337323 |
dewey-tens |
150 - Psychology 510 - Mathematics 600 - Technology |
hierarchy_top_title |
Journal of classification |
isfreeaccess_txt |
false |
familylinks_str_mv |
(DE-627)129337323 (DE-600)142885-8 (DE-576)014642832 |
title |
Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform |
ctrlnum |
(DE-627)OLC2144675023 (DE-He213)s00357-023-09437-z-p |
title_full |
Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform |
author_sort |
Kim, Minji |
journal |
Journal of classification |
journalStr |
Journal of classification |
lang_code |
eng |
isOA_bool |
false |
dewey-hundreds |
100 - Philosophy & psychology 500 - Science 600 - Technology |
recordtype |
marc |
publishDateSort |
2023 |
contenttype_str_mv |
txt |
container_start_page |
407 |
author_browse |
Kim, Minji Oh, Hee-Seok Lim, Yaeji |
container_volume |
40 |
class |
150 510 600 VZ 24,1 ssgn |
format_se |
Aufsätze |
author-letter |
Kim, Minji |
doi_str_mv |
10.1007/s00357-023-09437-z |
normlink |
(ORCID)0000-0002-8698-8667 |
normlink_prefix_str_mv |
(orcid)0000-0002-8698-8667 |
dewey-full |
150 510 600 |
title_sort |
zero-inflated time series clustering via ensemble thick-pen transform |
title_auth |
Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform |
abstract |
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 |
collection_details |
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 |
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 |
fullrecord_marcxml |
<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000naa a22002652 4500</leader><controlfield tag="001">OLC2144675023</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20240118100327.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">240118s2023 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s00357-023-09437-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2144675023</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s00357-023-09437-z-p</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">150</subfield><subfield code="a">510</subfield><subfield code="a">600</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">24,1</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kim, Minji</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Zero-Inflated Time Series Clustering Via Ensemble Thick-Pen Transform</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2023</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">ohne Hilfsmittel zu benutzen</subfield><subfield code="b">n</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Band</subfield><subfield code="b">nc</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="500" ind1=" " ind2=" "><subfield code="a">© The Author(s) 2023</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="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.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Clustering</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Multiscale method</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Newly confirmed COVID-19 case data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Step count data</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Thick-pen transform</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Zero-inflated time series data</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Oh, Hee-Seok</subfield><subfield code="4">aut</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Lim, Yaeji</subfield><subfield code="0">(orcid)0000-0002-8698-8667</subfield><subfield code="4">aut</subfield></datafield><datafield tag="773" ind1="0" ind2="8"><subfield code="i">Enthalten in</subfield><subfield code="t">Journal of classification</subfield><subfield code="d">Springer US, 1984</subfield><subfield code="g">40(2023), 2 vom: 12. Juni, Seite 407-431</subfield><subfield code="w">(DE-627)129337323</subfield><subfield code="w">(DE-600)142885-8</subfield><subfield code="w">(DE-576)014642832</subfield><subfield code="x">0176-4268</subfield><subfield code="7">nnns</subfield></datafield><datafield tag="773" ind1="1" ind2="8"><subfield code="g">volume:40</subfield><subfield code="g">year:2023</subfield><subfield code="g">number:2</subfield><subfield code="g">day:12</subfield><subfield code="g">month:06</subfield><subfield code="g">pages:407-431</subfield></datafield><datafield tag="856" ind1="4" ind2="1"><subfield code="u">https://doi.org/10.1007/s00357-023-09437-z</subfield><subfield code="z">lizenzpflichtig</subfield><subfield code="3">Volltext</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_USEFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SYSFLAG_A</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_OLC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-TEC</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-PHY</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-CHE</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OLC-BUB</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-BBI</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">SSG-OPC-MAT</subfield></datafield><datafield tag="912" ind1=" " ind2=" "><subfield code="a">GBV_ILN_2018</subfield></datafield><datafield tag="951" ind1=" " ind2=" "><subfield code="a">AR</subfield></datafield><datafield tag="952" ind1=" " ind2=" "><subfield code="d">40</subfield><subfield code="j">2023</subfield><subfield code="e">2</subfield><subfield code="b">12</subfield><subfield code="c">06</subfield><subfield code="h">407-431</subfield></datafield></record></collection>
|
score |
7.4002113 |