In statistics, Mahalanobis distance is a distance measure introduced by P. C. Mahalanobis in 1936. It is based on correlations between variables by which different patterns can be identified and analysed. It is a useful way of determining similarity of an unknown sample set to a known one. It differs from Euclidean distance in that it takes into account the correlations of the data set. Formally, the Mahalanobis distance from a group of values with mean and covariance matrix for a multivariate vector is defined as: and of the same distribution with the covariance matrix : where