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Minkowski distance [Explained]

Minkowski distance is a distance/ similarity measurement between two points in the normed vector space (N dimensional real space) and is a generalization of the Euclidean distance and the Manhattan distance. See the applications of Minkowshi distance and its visualization using an unit circle.

similarity measurement

Euclidean vs Manhattan vs Chebyshev Distance

Euclidean distance, Manhattan distance and Chebyshev distance are all distance metrics which compute a number based on two data points. All the three metrics are useful in various use cases and differ in some important aspects such as computation and real life usage.

similarity measurement

Euclidean distance (L2 norm)

Euclidean distance is the shortest distance between two points in an N dimensional space also known as Euclidean space. It is used as a common metric to measure the similarity between two data points and used in various fields such as geometry, data mining, deep learning and others.