Data Clustering via k-Means, k-Medoids, and PCA in R
applicable to non-normally distributed datasets
(e.g., in musical performance research)
Version 1.0 (08 March 2025)
compiled by Frithjof Vollmer

This script reproduces the command sequence for a dual cluster analysis of performance-related data, as employed in the analysis of measurement data from 57 recordings of Beethoven’s Violin Concerto, Op. 61 (1912–1956). Explanatory comments in German.

Core packages and functions:

• kmeans – for k-means cluster analysis
• cluster – standard package for more robust clustering methods
• pam – function from cluster for k-medoids analysis
• prcomp – for principal component analysis (PCA)

Output files (saved to the working directory):

• _Analyse – visualization of cluster centres
• _ANOVA – analysis of variance testing for significant differences among cluster vectors
• _Distanzen – PDF with scatter plot of data points per cluster and their distances from the cluster centres
• _sort_kM – CSV file assigning data points to clusters from the k-means analysis, ordered by distance from the cluster centre
• _sort_kMed – same as above, for the k-medoids analysis
• _CVect – CSV file containing the cluster centre vectors
• _mitCluster – original dataset as CSV, augmented with cluster membership for each data point
• _PCA – principal component analyses for cluster interpretation (visualizations in 2D and 3D scatter plots for 2PC and 3PC, respectively)