Algorithms for Hierarchical Clustering: An Overview, II

Fionn Murtagh, Pedro Contreras

Research output: Contribution to journalArticle

17 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. This review adds to the earlier version, Murtagh F, Contreras P. Algorithms for hierarchical clustering: an overview, Wiley Interdiscip Rev: Data Mining Knowl Discov 2012, 2, 86–97. WIREs Data Mining Knowl Discov 2017, 7:e1219. doi: 10.1002/widm.1219
LanguageEnglish
Article numbere1219
JournalWiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery
Volume7
Issue number6
Early online date4 Sep 2017
DOIs
Publication statusPublished - Nov 2017

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Clustering algorithms
Data mining
Self organizing maps

Cite this

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Algorithms for Hierarchical Clustering : An Overview, II. / Murtagh, Fionn; Contreras, Pedro.

In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, Vol. 7, No. 6, e1219, 11.2017.

Research output: Contribution to journalArticle

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