You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Consistent selection of the number of clusters via crossvalidation
Contributor:
WANG, JUNHUI
imprint:
Biometrika Trust, University College London, 2010
Published in:Biometrika
Language:
English
ISSN:
0006-3444
Origination:
Footnote:
Description:
<p>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. A novel 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.</p>