Sentence examples for common distance measures from inspiring English sources

The common distance measures that are used in clustering are Euclidean and Manhattan distance [48].

Common distance measures used for clustering either distinguish between these two effect types or ignore the difference between the two (e.g. correlation and absolute correlation coefficients).

The Euclidean metric is the most common and intuitive measure of distance; Manhattan is another common distance measure, but very rarely does either of them provide a close approximation of an empirical road network, such as an urban network.

Specifically, analysis of molecular variance (AMOVA; 32) was used to apportion genetic variation among different ecologically defined bacterial subpopulations, and the common genetic distance measure, F ST (33 ), was used to quantify short-term genetic distances among populations of bacteria from different host species and locations.

Absolute, squared distance measures are common for next event prediction, while log distance is commonly used for probabilistic settings prediction.

Before that, to be more consistent with the comparative genomics literature, where distance measures are more common than similarities, here we also propose a family-free DCJ distance.

Any measure of dissimilarity can be used, but distance measures are the most common.

The KL distance is a common choice for computing distances between probability densities, but we note that other distance measures could be implemented in a straightforward way.

Beside (l_{p}) distance measures, there exist several distance measures.

Define the following distance measures.

Other distance measures can also be applied.