Distance and clustering.

In k-means clustering code which uses Euclidean distance. I want to replace Euclidean distance by Mahalanobis distance.

Answer 1

[Image Analyst]

Can you program in the formula from here: https://en.wikipedia.org/wiki/Mahalanobis_distance

Or just use the mahal() function if you have the Statistics and Machine Learning Toolbox:

Description d = mahal(Y,X) computes the Mahalanobis distance (in squared units) of each observation in Y from the reference sample in matrix X. If Y is n-by-m, where n is the number of observations and m is the dimension of the data, d is n-by-1. X and Y must have the same number of columns, but can have different numbers of rows. X must have more rows than columns.

[John D'Errico]

hello_world has been asking the same question repeatedly. This is the 4th question I've seen from them on the exact same topic.