2016/08/26 by Jonas M B Haslbeck, Dirk U. Wulff, Haslbeck, Jonas M. B. +1
Computer Science · #Bayesian Methods and Mixture Models #Data Analysis with R #Data Mining Algorithms and Applications #FOS: Computer and information sciences #Machine Learning (stat.ML)
paper · pdf · doi:10.48550/arxiv.1608.07494
openalex publication_date 2016/08/26 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
We improve current instability-based methods for the selection of the number of clusters k in cluster analysis by developing a normalized cluster instability measure that corrects for the distribution of cluster sizes, a previously unaccounted driver of cluster instability. We show that our normalized instability measure outperforms current instability-based measures across the whole sequence of possible k and especially overcomes limitations in the context of large k. We also compare, for the first time, model-based and model-free approaches to determine cluster-instability and find their performance to be comparable. We make our method available in the R-package \verb+cstab+.