Clustering with Only Categorical Features

I am trying to do clustering with a bunch (24) of categorical features. I have done some research and found a lot of people recommending something such as K-Modes. I tried running K-Modes on my data and the best run had a cost of 27069.0, which seems pretty high.

Some of my features have only a few values, such as P, O, C, T, so I thought I could encode them. But others have many different values. Any tips on a clustering algorithm or some other approach? I would like to use Python.

EDIT: What about using Gower distance on the data and then using K-Means on that?

• @formicaman oops! You are in the wrong way! scipy.sparse.issparse is for the type of the variable, not about the concept of matrix sparsity! – OmG Mar 18 at 18:08