Multiparameter hierachial clustering methods
Date
2009
Authors
Carlsson, Gunnar
Memoli, Facundo
Editors
Advisors
Journal Title
Journal ISSN
Volume Title
Type:
Conference paper
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
Statement of Responsibility
Gunnar Carlsson and Facundon Mémoli
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
ICFS 2009
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.
School/Discipline
School of Computer Science
Dissertation Note
Provenance
Description
Access Status
Rights
© Springer-Verlag Berlin Heidelberg 2010