How to retrain a K-Modes model based on daily data? I have read that retraining a model depends highly on what you are trying to achieve. I am conscious that maybe I need to retrain my model daily and after a certain time I have to train the model again from scratch. That's no problem. The thing is, if I were to retrain a K-Modes model, I would need the previous centroids saved. I think this is also not a problem. Seeing the implementation of K-Modes here, the parameter init can be Huang or Cao. In the K-Means implementation of sklearn (see here), we can pass a ndarray of shape (n_clusters, n_features). I can't find this feature in the K-Modes implementation. So my specific questions are:


Is saving the previous centroids sufficient to retrain the model?
Is there a way to pass previous centroids in the K-Modes implementation I have mentioned? If not, should I implement my own K-Modes?
There's dedicated pypi package for incremental/online learning. It's called Creme and here's there repo. It contains KMeans implementation. Though under the hood, there's no incremental stuff going on, as you need all data in a pass (read more about Lloyd's algoritm here).