# Distance measures accounting for correlated variables in data

I'm doing clustering on data with 10 dimensional data. I recently had to revaluate my original cluster analysis because I found (by accident) that some of the variables are closely correlated. I assume that using any kind of linear distance measure, such as Euclidean, with correlated variables essentially adds increasing weight to the underlying component for every correlated variable that is used. This therefore could generate biases in the distance measures that I would not necessarily be aware of.

Are there any distance measures or clustering methods that account for this? Assuming I might have 1000 dimensional data then it would be hard to do manual examination of the correlations. Off the top of my head, I guess I could reduce down to the principle components that explain 90% of the variation in the data, then do the clustering and distance measures on these components?

I've attached a PCA plot with the loadings to provide an example of the correlated variables. Happy to recieve any answers that point out if I'm making the XY mistake.

• I assume that using any kind of linear distance measure, such as Euclidean, with correlated variables essentially adds increasing weight to... It is all enigmatic. Euclidean distance will be approximately the same in 2d (2-variable space) and in 1d (1st component space) if the variables are stroggly correlated (i.e. the data cloud is diagonal sausage). Variable correlations is not a general, universal obstacle for doing a cluster analysis. Apr 5 '17 at 8:22
• Aug 29 '17 at 13:54