Fast hierarchical clustering from the Baire distance

Pedro Contreras; Fionn Murtagh

doi:10.1007/978-3-642-10745-0_25

Fast hierarchical clustering from the Baire distance

Pedro Contreras, Fionn Murtagh

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

6 Citations (Scopus)

Abstract

The Baire or longest common prefix ultrametric allows a hierarchy, a multiway tree, or ultrametric topology embedding, to be constructed very efficiently. The Baire distance is a 1-bounded ultrametric. For high dimensional data, one approach for the use of the Baire distance is to base the hierarchy construction on random projections. In this paper we use the Baire distance on the Sloan Digital Sky Survey (SDSS, http://www.sdss.org) archive. We are addressing the regression of (high quality, more costly to collect) spectroscopic and (lower quality, more readily available) photometric redshifts. Nonlinear regression is used for mapping photometric and astrometric redshifts.

Original language	English
Title of host publication	Classification as a Tool for Research
Subtitle of host publication	Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft fur Klassifikation e.V.
Editors	Hermann Locarek-Junge, Claus Weihs
Publisher	Springer Berlin
Pages	235-243
Number of pages	9
ISBN (Electronic)	9783642107450
ISBN (Print)	9783642107443
DOIs	https://doi.org/10.1007/978-3-642-10745-0_25
Publication status	Published - 3 May 2010
Externally published	Yes
Event	11th Biennial Conference of the International Federation of Classification Societies with the 33rd Annual Conference of the German Classification Society : Classification as a Tool for Research - Dresden, Germany Duration: 13 Mar 2009 → 18 Mar 2009 Conference number: 11 / 33 http://www.ifcs2009.de/ (Link to Conference Information)

Publication series

Name	Studies in Classification, Data Analysis, and Knowledge Organization
Publisher	Springer
ISSN (Print)	1431-8814

Conference

Conference	11th Biennial Conference of the International Federation of Classification Societies with the 33rd Annual Conference of the German Classification Society
Abbreviated title	IFCS / GfKl 2009
Country/Territory	Germany
City	Dresden
Period	13/03/09 → 18/03/09
Internet address	http://www.ifcs2009.de/ (Link to Conference Information)

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10.1007/978-3-642-10745-0_25Licence: Unspecified

Cite this

Contreras, P., & Murtagh, F. (2010). Fast hierarchical clustering from the Baire distance. In H. Locarek-Junge, & C. Weihs (Eds.), Classification as a Tool for Research : Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft fur Klassifikation e.V. (pp. 235-243). (Studies in Classification, Data Analysis, and Knowledge Organization). Springer Berlin. https://doi.org/10.1007/978-3-642-10745-0_25

Contreras, Pedro ; Murtagh, Fionn. / Fast hierarchical clustering from the Baire distance. Classification as a Tool for Research : Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft fur Klassifikation e.V.. editor / Hermann Locarek-Junge ; Claus Weihs. Springer Berlin, 2010. pp. 235-243 (Studies in Classification, Data Analysis, and Knowledge Organization).

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abstract = "The Baire or longest common prefix ultrametric allows a hierarchy, a multiway tree, or ultrametric topology embedding, to be constructed very efficiently. The Baire distance is a 1-bounded ultrametric. For high dimensional data, one approach for the use of the Baire distance is to base the hierarchy construction on random projections. In this paper we use the Baire distance on the Sloan Digital Sky Survey (SDSS, http://www.sdss.org) archive. We are addressing the regression of (high quality, more costly to collect) spectroscopic and (lower quality, more readily available) photometric redshifts. Nonlinear regression is used for mapping photometric and astrometric redshifts.",

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Contreras, P & Murtagh, F 2010, Fast hierarchical clustering from the Baire distance. in H Locarek-Junge & C Weihs (eds), Classification as a Tool for Research : Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft fur Klassifikation e.V.. Studies in Classification, Data Analysis, and Knowledge Organization, Springer Berlin, pp. 235-243, 11th Biennial Conference of the International Federation of Classification Societies with the 33rd Annual Conference of the German Classification Society , Dresden, Lower Saxony, Germany, 13/03/09. https://doi.org/10.1007/978-3-642-10745-0_25

Fast hierarchical clustering from the Baire distance. / Contreras, Pedro; Murtagh, Fionn.

Classification as a Tool for Research : Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft fur Klassifikation e.V.. ed. / Hermann Locarek-Junge; Claus Weihs. Springer Berlin, 2010. p. 235-243 (Studies in Classification, Data Analysis, and Knowledge Organization).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AB - The Baire or longest common prefix ultrametric allows a hierarchy, a multiway tree, or ultrametric topology embedding, to be constructed very efficiently. The Baire distance is a 1-bounded ultrametric. For high dimensional data, one approach for the use of the Baire distance is to base the hierarchy construction on random projections. In this paper we use the Baire distance on the Sloan Digital Sky Survey (SDSS, http://www.sdss.org) archive. We are addressing the regression of (high quality, more costly to collect) spectroscopic and (lower quality, more readily available) photometric redshifts. Nonlinear regression is used for mapping photometric and astrometric redshifts.

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Contreras P, Murtagh F. Fast hierarchical clustering from the Baire distance. In Locarek-Junge H, Weihs C, editors, Classification as a Tool for Research : Proceedings of the 11th IFCS Biennial Conference and 33rd Annual Conference of the Gesellschaft fur Klassifikation e.V.. Springer Berlin. 2010. p. 235-243. (Studies in Classification, Data Analysis, and Knowledge Organization). doi: 10.1007/978-3-642-10745-0_25

Fast hierarchical clustering from the Baire distance

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