Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/77317
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dc.contributor.authorCarlsson, Gunnaren
dc.contributor.authorMemoli, Facundoen
dc.date.issued2009en
dc.identifier.citationClassification as a tool for research: Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft für Klassifikation e.V., Dresden, March 13-18, 2009 / H. Locarek-Junge and C. Weihs (eds.): pp.63-70en
dc.identifier.isbn9783642107443en
dc.identifier.isbn9783642107450en
dc.identifier.issn1431-8814en
dc.identifier.urihttp://hdl.handle.net/2440/77317-
dc.description.abstractWe propose an extension of hierarchical clustering methods, called multiparameter hierarchical clustering methods which are designed to exhibit sensitivity to density while retaining desirable theoretical properties. The input of the method we propose is a triple (X,d, ƒ), where (X,d) is a finite metric space and ƒ : X → R is a function defined on the data X, which could be a density estimate or could represent some other type of information. The output of our method is more general than dendrograms in that we track two parameters: the usual scale parameter and a parameter related to the function ƒ. Our construction is motivated by the methods of persistent topology (Edelsbrunner et al. 2000), the Reeb graph and Cluster Trees (Stuetzle 2003). We present both a characterization, and a stability theorem.en
dc.description.statementofresponsibilityGunnar Carlsson and Facundon Mémolien
dc.language.isoenen
dc.publisherSpringeren
dc.relation.ispartofseriesStudies in Classification, Data Analysis, and Knowledge Organizationen
dc.rights© Springer-Verlag Berlin Heidelberg 2010en
dc.subjectStatistical theory and methods; statistics and computing/statistics programs; data mining and knowledge discovery; artificial intelligence; roboticsen
dc.titleMultiparameter hierachial clustering methodsen
dc.typeConference paperen
dc.contributor.schoolSchool of Computer Scienceen
dc.contributor.conferenceInternational Federation of Classification Societies Conference and Annual Conference on the Gesellschaft für Klassifikation e.V. (11th/33rd : 2009 : Dresden, Germany)en
dc.contributor.conferenceICFS 2009en
dc.identifier.doi10.1007/978-3-642-10745-0_6en
Appears in Collections:Computer Science publications

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