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Hierarchical clustering is a bottom-up method, whereas k-means a divisive method.
From a computational perspective, some of the state-of-the-art algorithms are Louvain method [77, 78], LPA [48, 79], FNCA [49] and a voltage-based divisive method [80].
A divisive method takes a top-down approach.
The k-clique algorithm has demonstrated the advantages over the divisive method and agglomerative method.
An example of a divisive method is Newman and colleagues' hierarchical clustering for finding community structures in networks [ 53].
Phylogenetic information implemented in STAMP tool is based on two tree-building algorithms: an agglomerative method and a divisive method.
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Among hierarchical clustering methods, agglomerative methods provide the highest performance compared with the intermediate and low scores of respectively hybrid and divisive methods.
Well-known divisive methods include median-cut [11], octree [12], variance-based method [13], binary splitting method [14], and greedy orthogonal bipartitioning method [15].
The clustering can be based on either agglomerative methods, where all instances are assigned their own class and these classes are merged, or divisive methods, where everything is assigned to a single class and this class is subdivided.
The same holds true for divisive methods.
Hierarchical clustering is subdivided into 2 types: agglomerative methods and divisive methods.
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CEO of Professional Science Editing for Scientists @ prosciediting.com