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Clustering stability: an overview




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.

Author(s): von Luxburg, U.
Journal: Foundations and Trends in Machine Learning
Volume: 2
Number (issue): 3
Pages: 235-274
Year: 2010
Month: July
Day: 0

Department(s): Empirical Inference
Bibtex Type: Article (article)

Digital: 0
DOI: 10.1561/2200000008
Language: en
Organization: Max-Planck-Gesellschaft
School: Biologische Kybernetik

Links: PDF


  title = {Clustering stability: an overview},
  author = {von Luxburg, U.},
  journal = {Foundations and Trends in Machine Learning},
  volume = {2},
  number = {3},
  pages = {235-274},
  organization = {Max-Planck-Gesellschaft},
  school = {Biologische Kybernetik},
  month = jul,
  year = {2010},
  month_numeric = {7}