Please use this identifier to cite or link to this item: https://hdl.handle.net/2440/75412
Type: Journal article
Title: Characterization, stability and convergence of hierarchical clustering algorithms
Author: Carlsson, Gunnar
Memoli, Facundo
Citation: Journal of Machine Learning Research, 2010; 11(4):1425-1470
Publisher: MIT Press
Issue Date: 2010
ISSN: 1532-4435
School/Discipline: School of Computer Science
Statement of
Responsibility: 
clustering; hierarchical clustering; stability of clustering; Gromov-Hausdorff distance
Abstract: We study hierarchical clustering schemes under an axiomatic view. We show that within this framework, one can prove a theorem analogous to one of Kleinberg (2002), in which one obtains an existence and uniqueness theorem instead of a non-existence result. We explore further properties of this unique scheme: stability and convergence are established. We represent dendrograms as ultrametric spaces and use tools from metric geometry, namely the Gromov-Hausdorff distance, to quantify the degree to which perturbations in the input metric space affect the result of hierarchical methods.
Rights: © 2010 Gunnar Carlsson and Facundo Mémoli
Published version: http://jmlr.csail.mit.edu/papers/v11/carlsson10a.html
Appears in Collections:Computer Science publications

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