HDBSCAN = Automatical parameter selection for robust clustering of different inputs in Python

Question

I am trying to use HDBSCAN (Hamming metric) for clustering unlabeled categorical (binary) data. I would like to implement it in my code which provides HDBSCAN with the input dataframe of different shape every time it runs based on some external parameters selection etc. Therefor I expect different amount of clusters and their size in each run. Is there a way (method, piece of code etc.) that would calculate HDBSCAN parameters (mainly min_cluster_size, min_samples) automatically and individually every time it runs? Or..would you rather recommend me a different clustering algorithm? I have good experience with KMODES already. Thanks