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A Survey of Recent Advances in Hierarchical Clustering Algorithms
Author(s) -
Fionn Murtagh
Publication year - 1983
Publication title -
the computer journal
Language(s) - English
Resource type - Journals
eISSN - 1460-2067
pISSN - 0010-4620
DOI - 10.1093/comjnl/26.4.354
Subject(s) - cluster analysis , computer science , hierarchical clustering , hierarchical clustering of networks , pairwise comparison , data mining , cure data clustering algorithm , correlation clustering , brown clustering , canopy clustering algorithm , algorithm , artificial intelligence
It has often been asserted that since hierarchical clustering algorithms require pairwise interobject proximities, the complexity of these clustering procedures is at least O(N 2 ). Recent work has disproved this by incorporating efficient nearest neighbour searching algorithms into the clustering algorithms. A general framework for hierarchical, agglomerative clustering algorithms is discussed here, which opens up the prospect of much improvement on current, widely-used algorithms. This 'progress report' details new algorithmic approaches in this area, and reviews recent results.

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