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Home›Decision-making›Mahalanobis Distance — covariance-adjusted distance accounting for inter-criterion correlations
MCDMDistancecrisp

Mahalanobis Distance — covariance-adjusted distance accounting for inter-criterion correlations

MAHALANOBIS-DISTANCE (Mahalanobis Distance — covariance-adjusted distance accounting for inter-criterion correlations) is a distance multi-criteria decision-making (MCDM) method introduced by Mahalanobis, P. C. in 1936. It turns a decision matrix of alternatives scored on multiple criteria into a structured, reproducible result.

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When to use it

d ≥ 0; d=0 iff a=b. Mahalanobis Distance is symmetric.

Strengths & limitations

Strengths
  • Follows a transparent, reproducible computational procedure that can be audited step by step.
  • Handles multiple criteria of differing scales and units within a single decision matrix.
Limitations
  • Results depend on the chosen normalisation, weights, and parameter settings.

Sources

  1. Mahalanobis, P. C. (1936). Mahalanobis Distance. Proceedings of the National Institute of Sciences of India link ↗

How to cite this page

ScholarGate. (2026, June 2). Mahalanobis Distance — covariance-adjusted distance accounting for inter-criterion correlations. ScholarGate. https://scholargate.app/en/decision-making/mahalanobis-distance

Similar methods

DIST-MINKOWSKIPCA-WEIGHTDIST-CHEBYSHEVDIST-EUCLIDEANSD-WEIGHTGINI-WEIGHTDIST-HAMMINGDIST-MANHATTAN

Related reference concepts

Multidimensional ScalingLinear Discriminant AnalysisPrincipal Component AnalysisCanonical Correlation AnalysisMultivariate Normal DistributionDimension Reduction

Spotted an issue on this page? Report or suggest a fix →

ScholarGate — MAHALANOBIS-DISTANCE (Mahalanobis Distance — covariance-adjusted distance accounting for inter-criterion correlations). Retrieved 2026-07-21 from https://scholargate.app/en/decision-making/mahalanobis-distance · Dataset: https://doi.org/10.5281/zenodo.20539026
Quick facts
Originator
Mahalanobis, P. C.
Subfamily
Distance
Year
1936
Type
Distance (covariance-adjusted, correlation-aware)
Value Space
crisp
Uncertainty
None
Compensation
N/A
Rank Reversal
No
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