利用 MATLAB计算切氏距离 o pdist函数 Metric l euclidean t Euclidean distance(default). 命令行窗囗 >>D=pdist(x, chebychev') i euclidean Standardized Euclidean distance. Each coordinate difference between rows in x is scaled by dividing by the corresponding element of the standard deviation S=nanstd(X), To specify another value for s, use D=pdist (X,'seuclidean, s) 2.73806.55143.8134 ' cityblock' City block metric. I minkowski t Minkowski distance. The default exponent is 2. To specify a different exponent, >>D=pdist(x', chebychev') use D= pdist(X, 'minkowski, P), where P is a scalar positive value of the exponent 'chebychev Chebychev distance (maximum coordinate difference ). Immahalanobis' Mahalanobis distance, using the sample covariance of x as computed by nancov. To compute the distance with a different covariance, use D pdist(X, 'mahalanobis', C), where the matrix c is symmetric and positiv cosine t One minus the cosine of the included angle between points(treated as vectors i correlation One minus the sample correlation between points (treated as sequences of values) spearman' One minus the sample Spearmans rank correlation between observations (treated as sequences of values)
利用ＭＡＴＬＡＢ计算切氏距离 pdist函数
4广义欧氏距离( MAHALANOBIS DISTANCE) o设与X是来自均值向量为,协方差为∑的总体G 中的m维样品,则两个样品间的马氏距离为: d(M)=(X-X,y2(X1-X,) o实际应用中,总体协差阵未知可以用样本协差阵来代 替
4.广义欧氏距离（MAHALANOBIS DISTANCE） 设Xi与Xj是来自均值向量为 ，协方差为∑ 的总体G 中的p维样品，则两个样品间的马氏距离为： 实际应用中，总体协差阵未知可以用样本协差阵来代 替。 2 1 ( ) ( ) ( ) ij i j i j d M − = − − X X Σ X X
广义欧氏距离的优点在于: ①广义欧氏距离又称为马氏距离。马氏距离考 虑了观测变量之间的相关性 ②马氏距离还考虑了观测变量之间的变异性, 不再受各指标量纲的影响 ③将原始数据作线性变换后,马氏距离不变
广义欧氏距离的优点在于： ①广义欧氏距离又称为马氏距离。马氏距离考 虑了观测变量之间的相关性。 ②马氏距离还考虑了观测变量之间的变异性， 不再受各指标量纲的影响。 ③将原始数据作线性变换后，马氏距离不变
利用 MATLAB计算广义欧氏距离 o pdist函数 euclidean Euclidean distance(default) >>D=pdist(x, mahalanobis) i euclidean Standardized euclidean distance. each coordinate difference between rows in x is scaled by dividing by the corresponding element of the standard deviation S=nanstd (X) To specify another value for s, use D-=pdist (X,'seuclidean, s) 2.00002.00002.0000 'cityblock' City block metric. Minkowski Minkowski distance. The default exponent is 2. To specify a different exponent, useD pdist(X,'minkowski, P), where P is a scalar positive value of the exponent ' chebychev Chebychev distance(maximum coordinate difference). mahalanobis Mahalanobis distance, using the sample covariance of x as computed by nancov. To compute the distance with a different covariance, use D pdist (X,'mahalanobis, C), where the matrix C is symmetric and positive I cosine One minus the cosine of the included angle between points (treated as vectors) correlation One minus the sample correlation between points(treated as sequences of spearman One minus the sample Spearman s rank correlation between observations (treated as sequences of values)
利用ＭＡＴＬＡＢ计算广义欧氏距离 pdist函数
5.明考夫斯基距离( MINKOWSKI DISTANCE) o令d;表示向量与X的距离,则明考夫斯基的距离 公式为: d(q)=(∑Xk-X k=1 其中,当g取不同的值时,又会产生不同的距离
5.明考夫斯基距离（MINKOWSKI DISTANCE） 令dij 表示向量Xi与Xj的距离，则明考夫斯基的距离 公式为： 其中，当q取不同的值时，又会产生不同的距离。 1 / 1 ( ) ( ) p q q ij ik jk k d q X X = = −