[amp_mcq option1=”manhattan” option2=”eucledian” option3=”mean” option4=”minkowski” correct=”option1″]<\/p>\n
The most general form of distance is the Minkowski distance. It is a generalization of the Euclidean distance and the Manhattan distance. The Minkowski distance between two points $x$ and $y$ is defined as:<\/p>\n
$$d_p(x, y) = \\left(\\sum_{i=1}^n |x_i – y_i|^p\\right)^{1\/p}$$<\/p>\n
where $p$ is a real number greater than or equal to 1.<\/p>\n
The Euclidean distance is the special case of the Minkowski distance when $p = 2$. The Manhattan distance is the special case of the Minkowski distance when $p = 1$.<\/p>\n
The Minkowski distance is more general than the Euclidean distance and the Manhattan distance because it can be used to measure the distance between points in any number of dimensions. The Euclidean distance can only be used to measure the distance between points in two or three dimensions, and the Manhattan distance can only be used to measure the distance between points in one dimension.<\/p>\n
The Minkowski distance is also more general than the Euclidean distance and the Manhattan distance because it can be used to measure the distance between points that are not on the same line. The Euclidean distance and the Manhattan distance can only be used to measure the distance between points that are on the same line.<\/p>\n
The Minkowski distance is a useful tool for measuring the distance between points in a variety of applications. For example, it can be used to measure the distance between two cities, the distance between two objects in a computer graphics scene, or the distance between two genes in a DNA sequence.<\/p>\n","protected":false},"excerpt":{"rendered":"
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