Incremental modelling of kmeans in pyspark I have a large dataset and trained the model with kmeans for the first time. I  saved the model and pipeline used . Now again I started collecting data.  After sufficient data is collected using old model and pipeline I want to retrain the model on new collected data in Pyspark

Is it possible to use transfer learning in unsupervised learning algorithms (or clustering) like Kmeans in Pyspark? If so, how it can be done?
There's dedicated lib for incremental / online learning. It's called Creme. You can find it here. It has kmeans implemented, though I don't know how well it's suited for spark data sets.