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Algorithms for hierarchical clustering: an overview
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TLDR
A recently developed very efficient (linear time) hierarchical clustering algorithm is described, which can also be viewed as a hierarchical grid‐based algorithm.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. 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 Clusteringread more
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References
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Proceedings Article
A density-based algorithm for discovering clusters a density-based algorithm for discovering clusters in large spatial databases with noise
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