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Distance and clustering.

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hello_world
hello_world on 13 Aug 2016
Commented: John D'Errico on 13 Aug 2016
In k-means clustering code which uses Euclidean distance. I want to replace Euclidean distance by Mahalanobis distance.

Answers (1)

Image Analyst
Image Analyst on 13 Aug 2016
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.
  1 Comment
John D'Errico
John D'Errico on 13 Aug 2016
hello_world has been asking the same question repeatedly. This is the 4th question I've seen from them on the exact same topic.

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