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Can you please explain Manhattan distance in Artificial Intelligence?

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Manhattan distance in Artificial Intelligence is the sum of absolute distance between two points across all the axes. Suppose there are two points (p1, q1), (p2, q2) in two dimensions, then Manhattan distance between two points is

Manhattan distance = | p1-q1 | + | p2-q2 |

Manhattan distance is to calculate the similarity between data points or observations for classification problems and clustering.

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