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We locate the centers of the bubbles using the Euclidean distance-map construction and assign at each vertex position a continuous pinning field with a potential falling off as 1/r.
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In our experiments, we use the Euclidean distance function.
Differentially expressed gene heat maps were clustered by k-means clustering using the Euclidean distance as the distance and visualized using Java TreeView software (Saldanha, 2004).
The distance-based features are calculated using the Euclidean distance.
The distance between universities is computed using the Euclidean distance measure.
The length is calculated using the Euclidean distance metric.
The analysis sought 27 clusters using the Euclidean distance metric.
Lectins and species were clustered using the euclidean distance metric.
Clustering was done using the Euclidean distance measure and the complete linkage method.
The analysis was realized using the Euclidean distance and Ward's method.
Next, the genes were clustered using the euclidean distance and average linkage methods.
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