A Simple Permutation Test for Clusteredness
Hierarchical clustering is a popular method for finding structure in multivariate data, resulting in a binary tree constructed on the particular objects of the study, usually sampling units. The user faces the decision where to cut the binary tree in order to determine the number of clusters to interpret and there are various ad hoc rules for arriving at a decision. A simple permutation test is presented that diagnoses whether non-random levels of clustering are present in the set of objects and, if so, indicates the specific level at which the tree can be cut. The test is validated against random matrices to verify the type I error probability and a power study is performed on data sets with known clusteredness to study the type II error.
|Date of creation:||Apr 2011|
|Date of revision:|
|Contact details of provider:|| Postal: Ramon Trias Fargas, 25-27, 08005 Barcelona|
Phone: +34 93 542-1222
Fax: +34 93 542-1223
Web page: http://www.barcelonagse.eu
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Michael Greenacre, 2008. "Correspondence analysis of raw data," Economics Working Papers 1112, Department of Economics and Business, Universitat Pompeu Fabra, revised Jul 2009.
- Gordon, A. D., 1994. "Identifying genuine clusters in a classification," Computational Statistics & Data Analysis, Elsevier, vol. 18(5), pages 561-581, December.
When requesting a correction, please mention this item's handle: RePEc:bge:wpaper:555. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Bruno Guallar)
If references are entirely missing, you can add them using this form.