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Abstract:
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This paper illustrates different information visualization techniques (data visualization) applied to a classical composers’ database. In particular we present composers network graphs, heat maps and multidimensional scaling maps (the latter two obtained from a composer distance matrix), composers’ classification maps using support-vector machine and K-Nearest Neighbors algorithms, and dendrograms. All visualization techniques have been developed using Python programming and libraries. The ultimate objective is to enhance basic music education and interest in classical music by presenting information quickly and clearly, taking advantage of the human visual system’s ability to see patterns and trends.
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