First, I am not quite understand your overall approach. You built a model / (a clustering results) from data. But how good it is? (Why 5 clusters?). Without verifying the results are "good", why we should check with variables are contribute more on the unverified results?

Let's assume you checked the clustering results are good and just want to know which variables contribute more on the cluster center. I would first check the scale of the variables. If you are not scaling the variables, usually variables in large scale will be more important. Details can be found here.

Standardizing some features in K-Means

First, I am not quite understand your overall approach. You built a model / (a clustering results) from data. But how good it is? (Why 5 clusters?). Without verifying the results are "good", why we should check with variables are contribute more on the unverified results?

Let's assume you checked the clustering results are good and just want to know which variables contribute more on the cluster center. I would first check the scale of the variables. If you are not scaling the variables, usually variables in large scale will be more important. Details can be found here.

Standardizing some features in K-Means