欧氏距离和曼哈顿距离、K-means和EM算法对比
1、欧式距离和曼哈顿距离
欧式距离用于计算两点或多点之间的距离。
d ( x , y ) = ( x 1 − y 1 ) 2 + ( x 2 − y 2 ) 2 + ⋯ + ( x n − y n ) 2 = ∑ i = 1 n ( x i − y i ) 2 d(x, y) =\sqrt{\left(x_{1}-y_{1}\right)^{2}+\left(x_{2}-y_{2}\right)^{2}+\cdots+\left(x_{n}-y_{n}\right)^{2}}=\sqrt{\sum_{i=1}^{n}\left(x_{i}-y_{i}\right)^{2}} d(x,y)=(x1−y1)2+(x2−y2