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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| Veröffentlicht in: | Wiley interdisciplinary reviews. Data mining and knowledge discovery Jg. 7; H. 6; S. e1219 - n/a |
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| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Hoboken, USA
Wiley Periodicals, Inc
01.11.2017
Wiley Subscription Services, Inc |
| Schlagworte: | |
| ISSN: | 1942-4787, 1942-4795 |
| Online-Zugang: | Volltext |
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| Zusammenfassung: | 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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| Bibliographie: | ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 |
| ISSN: | 1942-4787 1942-4795 |
| DOI: | 10.1002/widm.1219 |