Estimating the Number of Clusters via Normalized Cluster Instability
Machine Learning
2018-10-16 v4
Abstract
We improve current instability-based methods for the selection of the number of clusters 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 and especially overcomes limitations in the context of large . 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+.
Cite
@article{arxiv.1608.07494,
title = {Estimating the Number of Clusters via Normalized Cluster Instability},
author = {Jonas M. B. Haslbeck and Dirk U. Wulff},
journal= {arXiv preprint arXiv:1608.07494},
year = {2018}
}