Algorithms for hierarchical clustering: An overview

Fionn Murtagh, Pedro Contreras

Research output: Contribution to journalArticlepeer-review

955 Citations (Scopus)


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
Issue number1
Publication statusPublished - 7 Dec 2012
Externally publishedYes


Dive into the research topics of 'Algorithms for hierarchical clustering: An overview'. Together they form a unique fingerprint.

Cite this