Identifying and exploiting ultrametricity

Fionn Murtagh

doi:10.1007/978-3-540-70981-7_30

Identifying and exploiting ultrametricity

Fionn Murtagh

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

8 Citations (Scopus)

Abstract

We begin with pervasive ultrametricity due to high dimensionality and/or spatial sparsity. How extent or degree of ultrametricity can be quantified leads us to the discussion of varied practical cases when ultrametricity can be partially or locally present in data. We show how the ultrametricity can be assessed in text or document collections, and in time series signals. In our presentation we also discussed applications to chemical information retrieval and to astrophysics, in particular observational cosmology.

Original language	English
Title of host publication	Advances in Data Analysis
Subtitle of host publication	Proceedings of the 30th Annual Conference of the Gesellschaft für Klassifikation
Editors	Reinhold Decker, Hans-J Lenz
Publisher	Springer Verlag
Pages	263-272
Number of pages	10
ISBN (Electronic)	9783540709817
ISBN (Print)	9783540709800
DOIs	https://doi.org/10.1007/978-3-540-70981-7_30
Publication status	Published - 1 Dec 2007
Externally published	Yes
Event	30th Annual Conference of the German Classification Society on Advances in Data Analysis - Berlin, Germany Duration: 8 Mar 2006 → 10 Mar 2006 Conference number: 30

Conference

Conference	30th Annual Conference of the German Classification Society on Advances in Data Analysis
Abbreviated title	GfKl 2006
Country/Territory	Germany
City	Berlin
Period	8/03/06 → 10/03/06

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Cite this

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title = "Identifying and exploiting ultrametricity",

abstract = "We begin with pervasive ultrametricity due to high dimensionality and/or spatial sparsity. How extent or degree of ultrametricity can be quantified leads us to the discussion of varied practical cases when ultrametricity can be partially or locally present in data. We show how the ultrametricity can be assessed in text or document collections, and in time series signals. In our presentation we also discussed applications to chemical information retrieval and to astrophysics, in particular observational cosmology.",

keywords = "analysis, Clustering, multivariate statistics, time series, Classification, data analysis, data mining, statistics",

author = "Fionn Murtagh",

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editor = "Reinhold Decker and Hans-J Lenz",

booktitle = "Advances in Data Analysis",

publisher = "Springer Verlag",

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note = "30th Annual Conference of the German Classification Society on Advances in Data Analysis, GfKl 2006 ; Conference date: 08-03-2006 Through 10-03-2006",

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Murtagh, F 2007, Identifying and exploiting ultrametricity. in R Decker & H-J Lenz (eds), Advances in Data Analysis: Proceedings of the 30th Annual Conference of the Gesellschaft für Klassifikation . Springer Verlag, pp. 263-272, 30th Annual Conference of the German Classification Society on Advances in Data Analysis, Berlin, Berlin, Germany, 8/03/06. https://doi.org/10.1007/978-3-540-70981-7_30

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N2 - We begin with pervasive ultrametricity due to high dimensionality and/or spatial sparsity. How extent or degree of ultrametricity can be quantified leads us to the discussion of varied practical cases when ultrametricity can be partially or locally present in data. We show how the ultrametricity can be assessed in text or document collections, and in time series signals. In our presentation we also discussed applications to chemical information retrieval and to astrophysics, in particular observational cosmology.

AB - We begin with pervasive ultrametricity due to high dimensionality and/or spatial sparsity. How extent or degree of ultrametricity can be quantified leads us to the discussion of varied practical cases when ultrametricity can be partially or locally present in data. We show how the ultrametricity can be assessed in text or document collections, and in time series signals. In our presentation we also discussed applications to chemical information retrieval and to astrophysics, in particular observational cosmology.

KW - analysis

KW - Clustering

KW - multivariate statistics

KW - time series

KW - Classification

KW - data analysis

KW - data mining

KW - statistics

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U2 - 10.1007/978-3-540-70981-7_30

DO - 10.1007/978-3-540-70981-7_30

M3 - Conference contribution

AN - SCOPUS:84879581168

SN - 9783540709800

SP - 263

EP - 272

BT - Advances in Data Analysis

A2 - Decker, Reinhold

A2 - Lenz, Hans-J

PB - Springer Verlag

T2 - 30th Annual Conference of the German Classification Society on Advances in Data Analysis

Y2 - 8 March 2006 through 10 March 2006

ER -

Identifying and exploiting ultrametricity

Abstract

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