Visualizing music similarity: clustering and mapping 500 classical music composers
Abstract This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500 × 500 composers’ similarity/distance matrix. The objective is to visualize or translate the similarity matrix into dendrograms and maps of classical (European art) music composers. We construct de...
Ausführliche Beschreibung
Autor*in: |
Georges, Patrick [verfasserIn] |
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Format: |
Artikel |
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Sprache: |
Englisch |
Erschienen: |
2019 |
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Schlagwörter: |
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Anmerkung: |
© The Author(s) 2019 |
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Übergeordnetes Werk: |
Enthalten in: Scientometrics - Springer International Publishing, 1978, 120(2019), 3 vom: 11. Juli, Seite 975-1003 |
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Übergeordnetes Werk: |
volume:120 ; year:2019 ; number:3 ; day:11 ; month:07 ; pages:975-1003 |
Links: |
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DOI / URN: |
10.1007/s11192-019-03166-0 |
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Katalog-ID: |
OLC2033222802 |
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10.1007/s11192-019-03166-0 doi (DE-627)OLC2033222802 (DE-He213)s11192-019-03166-0-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Georges, Patrick verfasserin (orcid)0000-0003-1944-1300 aut Visualizing music similarity: clustering and mapping 500 classical music composers 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2019 Abstract This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500 × 500 composers’ similarity/distance matrix. The objective is to visualize or translate the similarity matrix into dendrograms and maps of classical (European art) music composers. We construct dendrograms and maps for the Baroque, Classical, and Romantic periods, and a map that represents seven centuries of European art music in one single graph. Finally, we also use linear and non-linear canonical correlation analyses to identify variables underlying the dimensions generated by the MDS methodology. Mapping classical music composers Similarity measures Dendrograms Hierarchical clustering Multidimensional scaling Canonical correlation Music information retrieval Nguyen, Ngoc aut Enthalten in Scientometrics Springer International Publishing, 1978 120(2019), 3 vom: 11. Juli, Seite 975-1003 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:120 year:2019 number:3 day:11 month:07 pages:975-1003 https://doi.org/10.1007/s11192-019-03166-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 120 2019 3 11 07 975-1003 |
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10.1007/s11192-019-03166-0 doi (DE-627)OLC2033222802 (DE-He213)s11192-019-03166-0-p DE-627 ger DE-627 rakwb eng 050 370 VZ 11 ssgn Georges, Patrick verfasserin (orcid)0000-0003-1944-1300 aut Visualizing music similarity: clustering and mapping 500 classical music composers 2019 Text txt rdacontent ohne Hilfsmittel zu benutzen n rdamedia Band nc rdacarrier © The Author(s) 2019 Abstract This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500 × 500 composers’ similarity/distance matrix. The objective is to visualize or translate the similarity matrix into dendrograms and maps of classical (European art) music composers. We construct dendrograms and maps for the Baroque, Classical, and Romantic periods, and a map that represents seven centuries of European art music in one single graph. Finally, we also use linear and non-linear canonical correlation analyses to identify variables underlying the dimensions generated by the MDS methodology. Mapping classical music composers Similarity measures Dendrograms Hierarchical clustering Multidimensional scaling Canonical correlation Music information retrieval Nguyen, Ngoc aut Enthalten in Scientometrics Springer International Publishing, 1978 120(2019), 3 vom: 11. Juli, Seite 975-1003 (DE-627)13005352X (DE-600)435652-4 (DE-576)015591697 0138-9130 nnns volume:120 year:2019 number:3 day:11 month:07 pages:975-1003 https://doi.org/10.1007/s11192-019-03166-0 lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_OLC SSG-OLC-BUB SSG-OLC-HSW SSG-OPC-BBI GBV_ILN_4012 AR 120 2019 3 11 07 975-1003 |
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Abstract This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500 × 500 composers’ similarity/distance matrix. The objective is to visualize or translate the similarity matrix into dendrograms and maps of classical (European art) music composers. We construct dendrograms and maps for the Baroque, Classical, and Romantic periods, and a map that represents seven centuries of European art music in one single graph. Finally, we also use linear and non-linear canonical correlation analyses to identify variables underlying the dimensions generated by the MDS methodology. © The Author(s) 2019 |
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Abstract This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500 × 500 composers’ similarity/distance matrix. The objective is to visualize or translate the similarity matrix into dendrograms and maps of classical (European art) music composers. We construct dendrograms and maps for the Baroque, Classical, and Romantic periods, and a map that represents seven centuries of European art music in one single graph. Finally, we also use linear and non-linear canonical correlation analyses to identify variables underlying the dimensions generated by the MDS methodology. © The Author(s) 2019 |
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Abstract This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500 × 500 composers’ similarity/distance matrix. The objective is to visualize or translate the similarity matrix into dendrograms and maps of classical (European art) music composers. We construct dendrograms and maps for the Baroque, Classical, and Romantic periods, and a map that represents seven centuries of European art music in one single graph. Finally, we also use linear and non-linear canonical correlation analyses to identify variables underlying the dimensions generated by the MDS methodology. © The Author(s) 2019 |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">OLC2033222802</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230504042211.0</controlfield><controlfield tag="007">tu</controlfield><controlfield tag="008">200819s2019 xx ||||| 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s11192-019-03166-0</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)OLC2033222802</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-He213)s11192-019-03166-0-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">050</subfield><subfield code="a">370</subfield><subfield code="q">VZ</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">11</subfield><subfield code="2">ssgn</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Georges, Patrick</subfield><subfield code="e">verfasserin</subfield><subfield code="0">(orcid)0000-0003-1944-1300</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="0"><subfield code="a">Visualizing music similarity: clustering and mapping 500 classical music composers</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</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) 2019</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Abstract This paper applies clustering techniques and multi-dimensional scaling (MDS) analysis to a 500 × 500 composers’ similarity/distance matrix. The objective is to visualize or translate the similarity matrix into dendrograms and maps of classical (European art) music composers. We construct dendrograms and maps for the Baroque, Classical, and Romantic periods, and a map that represents seven centuries of European art music in one single graph. 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