Does anyone recommend a clustering algorithm that can also update existing clusters?

For instance say I have 1000 features that I cluster with algorithm A. I obtain another 500 features, I would like to use the existing cluster information without reclustering everything from the start.

Is there a clustering algorithm (ideally in sklearn and not k-means) that can handle this type of usage?

In one use case, the distance metric I plan on using will be jaccard since my data will be binary.

algorithm cluster clustering clustering algorithm datascience everything features information instance k-means sklearn update