Howtosignalprocessing
- When should we use Mahalanobis distance?
- How can you measure distances between two clusters?
- Why Mahalanobis distance is better than Euclidean distance?
- How is cluster analysis used to group variables?
When should we use Mahalanobis distance?
The Mahalanobis distance is one of the most common measures in chemometrics, or indeed multivariate statistics. It can be used to determine whether a sample is an outlier, whether a process is in control or whether a sample is a member of a group or not.
How can you measure distances between two clusters?
The distance between the two clusters is the maximum between the two clusters. Obviously, 8 and −5 are the furthest in your scenario. For instance, D(8,0) = |8-0| = only 8.
Why Mahalanobis distance is better than Euclidean distance?
Mahalanobis and Euclidean Distance
But, MD uses a covariance matrix unlike Euclidean. Because of that, MD works well when two or more variables are highly correlated and even if their scales are not the same . But, when two or more variables are not on the same scale, Euclidean distance results might misdirect.
How is cluster analysis used to group variables?
Cluster analysis is a technique to group similar observations into a number of clusters based on the observed values of several variables for each individual. Cluster analysis is similar in concept to discriminant analysis.
