“distance measure”的概念、定义、翻译、参考文献-科学参考

distance measure

Statistics

A measure of the distance between points in multidimensional space (also called a metric). Distance measures are used with techniques such as cluster analysis and multidimensional scaling. The two measures most commonly used are Euclidean distance and the city-block metric.
Euclidean distance is the straight-line distance between two points. In d dimensions, if the positions of P and Q are given by the coordinates (p₁, p₂,…, p_d) and (q₁, q₂,…, q_d) then the Euclidean distance between P and Q is given by $distance measure$ The city-block metric in two dimensions measures the distance between two points in a city if, for example, the only directions in which one could travel were north-south and east-west. It is also called the Manhattan distance. In d dimensions the city-block distance between P and Q is given by $distance measure$ A generalization of the previous measures is the Minkowski distance $distance measure$ where k is a positive integer. Euclidean distance is the Minkowski distance of order 2, and Manhattan distance is the Minkowski distance of order 1.
Distance measure. These are the two simplest of an infinite number of possible measures of distance.
The Chebyshev distance is the largest difference over the d dimensions $distance measure$ Two other measures that have been proposed are the Canberra distance, given by $distance measure$ and, in the context of counts of organisms, the Bray-Curtis distance (also called the Sorensen distance): $distance measure$

单词	distance measure
释义	distance measure Statistics A measure of the distance between points in multidimensional space (also called a metric). Distance measures are used with techniques such as cluster analysis and multidimensional scaling. The two measures most commonly used are Euclidean distance and the city-block metric. Euclidean distance is the straight-line distance between two points. In d dimensions, if the positions of P and Q are given by the coordinates (p₁, p₂,…, p_d) and (q₁, q₂,…, q_d) then the Euclidean distance between P and Q is given by $distance measure$ The city-block metric in two dimensions measures the distance between two points in a city if, for example, the only directions in which one could travel were north-south and east-west. It is also called the Manhattan distance. In d dimensions the city-block distance between P and Q is given by $distance measure$ A generalization of the previous measures is the Minkowski distance $distance measure$ where k is a positive integer. Euclidean distance is the Minkowski distance of order 2, and Manhattan distance is the Minkowski distance of order 1. Distance measure. These are the two simplest of an infinite number of possible measures of distance. The Chebyshev distance is the largest difference over the d dimensions $distance measure$ Two other measures that have been proposed are the Canberra distance, given by $distance measure$ and, in the context of counts of organisms, the Bray-Curtis distance (also called the Sorensen distance): $distance measure$
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