The Mahalanobis distance is a measure of the distance between a point and a probability distribution , introduced by P. C. Mahalanobis in 1936. [1] The mathematical details of …

The Mahalanobis distance measures distance relative to the centroid — a base or central point which can be thought of as an overall mean for multivariate data.

P.C. Mahalanobis, Indian statistician who devised the Mahalanobis distance and was instrumental in formulating India’s strategy for industrialization in the Second Five-Year Plan (1956–61).

May 14, 2025 · Explore comprehensive techniques to compute and interpret the Mahalanobis distance in multivariate analysis for reliable outlier detection.

Mahalanobis distance is a multivariate distance metric that measures how far a point is from the center of a distribution, taking into account correlations between variables.

Mahalanobis distance is an effective multivariate distance metric that measures the distance between a point (vector) and a distribution. It has excellent applications in multivariate …

Mahalanobis distance is a more sophisticated measure than Euclidean distance for identifying outliers. While Euclidean distance simply calculates the straight-line distance between a point …

Mahalanobis distance is widely used in cluster analysis and classification techniques. It is closely related to Hotelling's T-square distribution used for multivariate statistical testing and Fisher's …

This distance is based on the correlation between variables or the variance–covariance matrix. It differs from the Euclidean distance in that it takes into account the correlation of the data set …

Dec 18, 2025 · The Mahalanobis distance is a measure of the distance between a point P and a probability distribution D, introduced by P. C. Mahalanobis in 1936. The mathematical …