The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Algorithms for hierarchical clustering: an overview
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. © 2011 Wiley Periodicals, Inc. This article is categorized under: Algorithmic Development > Hierarchies and Trees Technologies > Structure Discovery and Clustering
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Ankerst, M. (1999) · doi:10.1145/304181.304187 · Cited by 3,982
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Metadata
- Authors
- Murtagh, F., & Contreras, P.
- Venue
- WIREs Data Mining and Knowledge Discovery
- Publisher
- Wiley
- Published
- 2011-12-07
- Volume
- 2
- Issue
- 1
- Pages
- 86-97
- ISSN
- 1942-4787 (ISSN-L)
- 1942-4795
- OpenAlex
- W2158703410
- Cited by
- 1,874
- References
- 1
- FWCI
- 9.17
- Type
- journal article
- Language
- en
- Open access
- closed
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