How to give a higher importance to certain features in a (k-means) clustering model? I am clustering data with numeric and categorical variables. To process the categorical variables for the cluster model, I create dummy variables. However, I feel like this results in a higher importance for these dummy variables because multiple dummy variables represent one categorical variable.

For example, I have a categorical variable Airport that will result in multiple dummy variables: LAX, JFK, MIA and BOS. Now suppose I also have a numeric Temperature variable. I also scale all variables to be between 0 and 1. Now my Airport variable seems to be 4 times more important than the Temperature variable, and the clusters will be mostly based on the Airport variable.

My problem is that I want all variables to have the same importance. Is there a way to do this? I was thinking of scaling the variables in a different way but I don't know how to scale them in order to give them the same importance.
You cannot really use k-means clustering if your data contains categorical variables since k-means uses Euclidian distance which will not make a lot of sense with categorical variables. Check out the answers to this similar question.

You can use the following rules for performing clustering with k-means or one of its derivates:

If your data contains only metric variables:

Scale the data and use k-means (R) (Python).

If your data contains only categorical variables:

Use k-modes (R) (Python).

If your data contains categorical and metric variables:

Scale the metric variables and use k-prototypes (R) (Python).