Algorithms for hierarchical clustering: an overview

By Fionn Murtagh, Pedro Contreras. WIREs Data Mining Knowl. Discov., 2012

1,909 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Artificial Intelligence and Digital Pathology: Challenges and Opportunities (2018, positive): “[49,50] We need to rediscover the potential of unsupervised algorithms, such as self‐organizing maps[51] and hierarchical clustering,[52] and adequately integrate them into the workflow of routine pathology practice.”
Comprehensive survey on hierarchical clustering algorithms and the recent developments (2022, background): “…by Lance–Williams dissimilarity update formula (Lance and Williams 1967), where the parameters can define the different agglomerative criterion (Reddy and Vinzamuri 2013; Murtagh and Contreras 2012) and its generalizations (Wishart 1969; Batagelj 1988; Jambu et al. 1989; Székely and Rizzo 2005).”
Algorithms for hierarchical clustering: an overview, II (2017, methodology): “It is often helpful to distinguish between method, involving a compactness criterion and the target structure of a two-way tree representing the partial order on subsets of the power set, as opposed to an implementation, which relates to the details of the algorithm used.(1) As with many other multi”
p-Adic mathematical physics: the first 30 years (2017, methodology): “For some other applications of p-adic, hierarchical and wavelet methods in data analysis and related fields see [245, 246, 45, 111, 244, 208, 209, 210, 211].”