I'm looking to cluster data on apartments. I have the following variables for each apartment:
- Number of bathrooms
- Number of bedrooms
- Amenities (washer, gym, etc.)
The problem I'm struggling with is the Amenities variable has several levels (like over 50). If I one-hot encode this variable and then scale all the features, I would essentially be making each level of Amenities equally important (to the clustering algorithm) as some of the other variables like price (which might obviously be highly correlated with the other features) and location, which doesn't seem like the best approach for clustering. Should I possibly only include a few amenities that I "think" are most important or most common?
Also, I know that K-Means isn't the best approach for location data because it looks to minimize variance rather than geodetic distance. Could someone possibly give any insight as to the best approach to this problem?