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Visualizing music similarity: clustering and mapping 500 classical music composers | SpringerLink

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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.

Visualizing music similarity: clustering and mapping 500 classical music composers Open access Published: 11 July 2019 Volume 120 , pages 975–1003 ( 2019 ) Cite this article You have full access to this open access article Download PDF Save article View saved research Scientometrics Aims and scope Submit manuscript Visualizing music similarity: clustering and mapping 500 classical music composers Download PDF 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 trans

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