Clustering Stability: An Overview
Machine Learning
2010-07-08 v1
Abstract
A popular method for selecting the number of clusters is based on stability arguments: one chooses the number of clusters such that the corresponding clustering results are "most stable". In recent years, a series of papers has analyzed the behavior of this method from a theoretical point of view. However, the results are very technical and difficult to interpret for non-experts. In this paper we give a high-level overview about the existing literature on clustering stability. In addition to presenting the results in a slightly informal but accessible way, we relate them to each other and discuss their different implications.
Cite
@article{arxiv.1007.1075,
title = {Clustering Stability: An Overview},
author = {Ulrike von Luxburg},
journal= {arXiv preprint arXiv:1007.1075},
year = {2010}
}