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Type: Conference paper
Title: Multiparameter hierachial clustering methods
Author: Carlsson, Gunnar
Memoli, Facundo
Citation: Classification 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.63-70
Publisher: Springer
Issue Date: 2009
Series/Report no.: Studies in Classification, Data Analysis, and Knowledge Organization
ISBN: 9783642107443
ISSN: 1431-8814
Conference Name: International Federation of Classification Societies Conference and Annual Conference on the Gesellschaft für Klassifikation e.V. (11th/33rd : 2009 : Dresden, Germany)
ICFS 2009
School/Discipline: School of Computer Science
Statement of
Gunnar Carlsson and Facundon Mémoli
Abstract: We 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.
Keywords: Statistical theory and methods; statistics and computing/statistics programs; data mining and knowledge discovery; artificial intelligence; robotics
Rights: © Springer-Verlag Berlin Heidelberg 2010
DOI: 10.1007/978-3-642-10745-0_6
Appears in Collections:Computer Science publications

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