# What clustering algorithms work best for datasets with only binary categorical features?

I have a dataset with a lot of binary categorical features and a single continuous target value. I would like to cluster them, but I am not quite sure what to use.

In the past, I have used DBSCAN for something similar and it worked well, but that dataset also had lots of continuous features.

Do you have any tips and suggestions?

Would you suggest matrix factorization and then cluster?

• By "binary categorical (one-hot encoded)", do you mean that you have $n$ features, and, for each input, only one of them is $1$ and all the others are $0$?
– nbro
May 15, 2022 at 15:59
• @nbro thanks for the comment! No, I have n features and any combination of 0, 1s among them is possible. i.e. if n = 3, I may have, 000 or 010, or 111, etc. May 15, 2022 at 16:02
• Ok, then I don't think that's called one-hot encoded. 1-hot encoded is what I described, I think.
– nbro
May 15, 2022 at 16:07
• @nbro ah, apologies, I meant to convey that the individual features can only be either 0 or 1. I'll edit. May 15, 2022 at 16:08