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Algorithms for hierarchical clustering: an overview
Author(s) -
Murtagh Fionn,
Contreras Pedro
Publication year - 2011
Publication title -
wiley interdisciplinary reviews: data mining and knowledge discovery
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.506
H-Index - 47
eISSN - 1942-4795
pISSN - 1942-4787
DOI - 10.1002/widm.53
Subject(s) - hierarchical clustering , hierarchical clustering of networks , cluster analysis , computer science , brown clustering , grid , data mining , implementation , hierarchical database model , single linkage clustering , cure data clustering algorithm , correlation clustering , theoretical computer science , artificial intelligence , mathematics , software engineering , geometry
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. © 2011 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Hierarchies and Trees Technologies > Structure Discovery and Clustering
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