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The same holds true for divisive methods.
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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.
Hierarchical clustering is subdivided into 2 types: agglomerative methods and divisive methods.
In hierarchical clustering, there is another class of methods called divisive methods that construct hierarchical trees by removing edges.
Divisive methods attempt to find the least similar connected pairs of vertices from the network of interest and then remove the edges between the pairs.
An example of a divisive method is Newman and colleagues' hierarchical clustering for finding community structures in networks [ 53].
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 hclust function from Bioconductor was used for the agglomerative techniques and the diana function from the cluster package from Bioconductor was used to run the divisive method.
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