Consistent selection of the number of clusters via crossvalidation

Junhui Wang

Biometrika, 2010, vol. 97, issue 4, 893-904

Abstract: In cluster analysis, one of the major challenges is to estimate the number of clusters. Most existing approaches attempt to minimize some distance-based dissimilarity measure within clusters. This article proposes a novel selection criterion that is applicable to all kinds of clustering algorithms, including distance based or non-distance based algorithms. The key idea is to select the number of clusters that minimizes the algorithm's instability, which measures the robustness of any given clustering algorithm against the randomness in sampling.Anovel estimation scheme for clustering instability is developed based on crossvalidation. The proposed selection criterion's effectiveness is demonstrated on a variety of numerical experiments, and its asymptotic selection consistency is established when the dataset is properly split. Copyright 2010, Oxford University Press.

Date: 2010
References: Add references at CitEc
Citations: View citations in EconPapers (29)

Downloads: (external link)
http://hdl.handle.net/10.1093/biomet/asq061 (application/pdf)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:oup:biomet:v:97:y:2010:i:4:p:893-904

Ordering information: This journal article can be ordered from
https://academic.oup.com/journals

Access Statistics for this article

Biometrika is currently edited by Paul Fearnhead

More articles in Biometrika from Biometrika Trust Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK.
Bibliographic data for series maintained by Oxford University Press ().