Abstract
Music charts provide a simple statistic of sold records. Web 2.0 provides social networks, where detailed information from listeners is available. In particular, there are keywords, so called tags, that are given by the network members to classify songs into genres.
An important topic are music fads, i.e., small time intervals of a few weeks with a strong presence of similar music genres. We introduce a distance on the weekly music charts to uncover music fads. Fads are visualized using Emergent Self Organizing Maps (ESOM). They are automatically found by analysing the progress of the impact of music genres. This algorithm does not rely on an estimation of the number of fads. Dominant genres of the fads were found to characterize them.
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© 2009 Springer-Verlag Berlin Heidelberg
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Meyer, F., Ultsch, A. (2009). Finding Music Fads by Clustering Online Radio Data with Emergent Self Organizing Maps. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_38
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DOI: https://doi.org/10.1007/978-3-642-01044-6_38
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