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In [3] this is made using the euclidean metric.
Cluster frequency through graph-cluster contrast We explored X using the Euclidean metric and the average link method.
This set S fe was compared with the set S in containing the 10 closest neighbours of in the input space (these neighbours derived using the Euclidean metric).
We claim that ({x_{n}}overset{d}{rightarrow}0) using the Euclidean metric (d ( x,y ) =vert x-y vert ) for all (x,yin X).
We run the k‐Means algorithm (using the Euclidean metric) and the spectral clustering algorithm on the selected datasets –with the goal of getting new insights on the results of the partitioning procedure defined in DAN.
To prove it, let u ∈ R and let { x n } ⊂ R be a sequence such that { x n } → q u and ( x n, x n + 1 ) ∈ M for all n ∈ N 0. In particular, { x n } → u using the Euclidean metric.
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The weighted kNN algorithm uses the Euclidean metric to measure distances between molecules.
We also carried out experiments using the Euclidean distance metric and the Bhattacharyya distance metric but there was no significant difference in terms of the classification accuracies achieved.
The closest point is then found by performing an exhaustive search within the hypercube using the Euclidean distance metric.
Complete linkage hierarchical clustering was performed on data scaled so that all probe-sets shared the same mean and variance, using the euclidean distance metric in the stats package in R [21].
The length is calculated using the Euclidean distance metric.
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