Applying subgroup discovery for the analysis of string quartet movements

Authors:
Jonatan Taminau;Ruben Hillewaere;Stijn Meganck;Darrell Conklin;Ann Nowé;Bernard Manderick
Affiliations:
Vrije Universiteit Brussel, Brussels, Belgium;Vrije Universiteit Brussel, Brussels, Belgium;Vrije Universiteit Brussel, Brussels, Belgium;Universidad del País Vasco, San Sebastián, Spain;Vrije Universiteit Brussel, Brussels, Belgium;Vrije Universiteit Brussel, Brussels, Belgium
Venue:
Proceedings of 3rd international workshop on Machine learning and music
Year:
2010

Citing 5
Cited 0

Explora: a multipattern and multistrategy discovery assistant

Advances in knowledge discovery and data mining
The CN2 Induction Algorithm

Machine Learning
An Algorithm for Multi-relational Discovery of Subgroups

PKDD '97 Proceedings of the First European Symposium on Principles of Data Mining and Knowledge Discovery
Subgroup Discovery with CN2-SD

The Journal of Machine Learning Research
Discovery of distinctive patterns in music

Intelligent Data Analysis - Machine Learning and Music

Quantified Score

Hi-index	0.00

Visualization

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.