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The most commonly used distance is the Euclidean distance: (1).
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The classic and still commonly used distance was introduced in [ 33] as the total cost of duplications and losses (and transfers, if allowed).
Some of the commonly used distance metrics are mentioned below.
Currently, the commonly used distance metrics are Euclidean distance [ 32], Mahalanobis distance [ 28], Manhattan distance [ 33], Chebyshev distance [ 34], and so on.
Complete linkage hierarchical clustering using Euclidian distance was used to compare groups to each other.
These are traditionally used distances, but they are often chosen empirically [32].
Chi-square was the used distance.
Testing a cannon to learn it's use and distance is a good idea before you bring it into combat.
Hierarchical clustering using Euclidean distance was performed with TMeV 4.5 [16].
Hierarchical cluster analysis using Euclidean distance was performed to cluster genes and samples for heatmap.
Hierarchal clustering using Euclidian distances was used to distinguish among cultivars and between COD.
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