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Applying Subgroup Discovery for the Analysis of String Quertet Movements Authors: J. Taminau, R. Hillewaere, S. Meganck, D. Conklin, B. Manderick and A. Nowé Publication Year: 2011 Pages: 29-32
Abstract: Descriptive and predictive analyses of symbolic music data assist in understanding the properties that characterize spe- cific genres, movements and composers. Subgroup Discov- ery, a machine learning technique lying on the intersection between these types of analysis, is applied on a dataset of string quartet movements composed by either Haydn or Mozart. The resulting rules describe subgroups of move- ments for each composer, which are examined manually, and we investigate whether these subgroups correlate with meta- data such as type of movement or period. In addition to this descriptive analysis, the obtained rules are used for the pre- dictive task of composer classification results are compared with previous results on this corpus.
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