@Article{          taminau.ea2010-applying,
    author       = {Taminau, Jonatan and Hillewaere, Ruben and Meganck, Stijn
                   and Conklin, Darrell and Now{\'{e}}, Ann and Manderick,
                   Bernard},
    year         = {2010},
    title        = {Applying subgroup discovery for the analysis of string
                   quartet movements},
    abstract     = {Descriptive and predictive analyses of symbolic music
                   data assist in understanding the properties that
                   characterize specific genres, movements and composers.
                   Subgroup Discovery, 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 movements for each composer, which are
                   examined manually, and we investigate whether these
                   subgroups correlate with metadata such as type of movement
                   or period. In addition to this descriptive analysis, the
                   obtained rules are used for the predictive task of
                   composer classification; results are compared with
                   previous results on this corpus.},
    doi          = {10.1145/1878003.1878014},
    isbn         = {9781450301619},
    journal      = {MML'10 - Proceedings of the 3rd ACM International
                   Workshop on Machine Learning and Music, Co-located with
                   ACM Multimedia 2010},
    keywords     = {Global features,Subgroup discovery,computational
                   musicology},
    mendeley-tags= {computational musicology},
    number       = {May 2014},
    pages        = {29--32}
}

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