Algorithms for hierarchical clustering

An overview

Fionn Murtagh, Pedro Contreras

Research output: Contribution to journalArticle

200 Citations (Scopus)

Abstract

We survey agglomerative hierarchical clustering algorithms and discuss efficient implementations that are available in R and other software environments. We look at hierarchical self-organizing maps, and mixture models. We review grid-based clustering, focusing on hierarchical density-based approaches. Finally, we describe a recently developed very efficient (linear time) hierarchical clustering algorithm, which can also be viewed as a hierarchical grid-based algorithm.

Original languageEnglish
Pages (from-to)86-97
Number of pages12
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Volume2
Issue number1
DOIs
Publication statusPublished - 7 Dec 2012
Externally publishedYes

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Clustering algorithms
Self organizing maps

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Algorithms for hierarchical clustering : An overview. / Murtagh, Fionn; Contreras, Pedro.

In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 2, No. 1, 07.12.2012, p. 86-97.

Research output: Contribution to journalArticle

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