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Yet, as mentioned by Smith ( 2000), most people explore new subjects by starting with the familiar, and in the case of music, this may mean hearing a composer that one likes and searching for more music of the same type. Many introductions to the classical music world are in the business of inculcation through lists of ‘mandatory’ composers and compositions to explore. However, the vast store of music makes the problem of what, exactly, to listen to, even more acute than during the pre-digital age. Through digital music files and streaming, classical music consumers have access to an enormous range of composers and styles of classical music. Before embarking on this mapping project, it may be useful to provide some background on the motivation for building a composers’ similarity matrix. Hence, ‘visualizing’ or ‘translating’ the similarity matrix into clusters and mapping of composers is an important communication tool.
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Sheer dimension prevents easy reporting of the results in a standard article.
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The number of composers analysed is 500 and the matrix is of dimension 500 × 500, leading to 250,000 pairwise (bilateral) composers’ similarity indices. This paper uses several techniques that permit to visualize and graph some aspects of a Classical composers’ similarity matrix computed by Georges ( 2017).