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dc.contributor.authorFang, Yixin
dc.contributor.authorWang, Junhui
dc.date.accessioned2012-08-21T03:24:26Z
dc.date.available2012-08-21T03:24:26Z
dc.date.issued2012-03
dc.identifier.bibliographicCitationFang, Y. X. & Wang, J. H. 2012. Selection of the number of clusters via the bootstrap method. Computational Statistics & Data Analysis, 56(3): 468-477. DOI: 10.1016/j.csda.2011.09.003en
dc.identifier.issn0167-9473
dc.identifier.otherDOI: 10.1016/j.csda.2011.09.003
dc.identifier.urihttp://hdl.handle.net/10027/8612
dc.descriptionNOTICE: this is the author’s version of a work that was accepted for publication in Computational Statistics and Data Analysis. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Computational Statistics and Data Analysis, Vol 56, Issue 3, (MAR 1 2012). DOI: 10.1016/j.csda.2011.09.003en
dc.description.abstractHere the problem of selecting the number of clusters in cluster analysis is considered. Recently, the concept of clustering stability, which measures the robustness of any given clustering algorithm, has been utilized in Wang (2010) for selecting the number of clusters through cross validation. In this manuscript, an estimation scheme for clustering instability is developed based on the bootstrap, and then the number of clusters is selected so that the corresponding estimated clustering instability is minimized. The proposed selection criterion’s effectiveness is demonstrated on simulations and real examples.en
dc.language.isoen_USen
dc.publisherElsevieren
dc.subjectCluster analysisen
dc.subjectK-meansen
dc.subjectSpectral clusteringen
dc.subjectStabilityen
dc.titleSelection of the number of clusters via the bootstrap methoden
dc.typeArticleen


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