Algorithms for hierarchical clustering: an overview, II

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 approache...

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Bibliographic Details
Published in:Wiley interdisciplinary reviews. Data mining and knowledge discovery Vol. 7; no. 6; pp. e1219 - n/a
Main Authors: Murtagh, Fionn, Contreras, Pedro
Format: Journal Article
Language:English
Published: Hoboken, USA Wiley Periodicals, Inc 01.11.2017
Wiley Subscription Services, Inc
Subjects:
Algorithms
Clustering
Data mining
Self organizing maps
ISSN:1942-4787, 1942-4795
Online Access:Get full text
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Summary: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 This article is categorized under: Algorithmic Development > Hierarchies and Trees Technologies > Classification Technologies > Structure Discovery and Clustering Hierarchical clustering of Aristotle categories. Using text mining.
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ISSN:1942-4787
1942-4795
DOI:10.1002/widm.1219