A simple permutation test for clusteredness

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• dc.contributor.author Greenacre, Michaelca
• dc.contributor.other Universitat Pompeu Fabra. Departament d'Economia i Empresa
• dc.date.accessioned 2017-07-26T10:50:29Z
• dc.date.available 2017-07-26T10:50:29Z
• dc.date.issued 2011-04-01
• dc.date.modified 2017-07-23T02:13:51Z
• dc.description.abstract 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.
• dc.format.mimetype application/pdfca
• dc.identifier https://econ-papers.upf.edu/ca/paper.php?id=1271
• dc.identifier.uri http://hdl.handle.net/10230/19856
• dc.language.iso eng
• dc.relation.ispartofseries Economics and Business Working Papers Series; 1271
• dc.rights L'accés als continguts d'aquest document queda condicionat a l'acceptació de les condicions d'ús establertes per la següent llicència Creative Commons
• dc.rights.accessRights info:eu-repo/semantics/openAccess
• dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/
• dc.subject.keyword hierarchical clustering
• dc.subject.keyword distance
• dc.subject.keyword permutation test
• dc.subject.keyword Statistics, Econometrics and Quantitative Methods
• dc.title A simple permutation test for clusterednessca
• dc.type info:eu-repo/semantics/workingPaper