# How k-means computes cluster centroids differently for each distance metric?

K-means computes cluster centroids differently for each distance metric. I don't know why the way of computing the centroid is dependent of the distance measure.

I don't know how we compute the centroid for manhattan distance and its difference with the computing the centroid for euclidean distance?