K-modes implementation in pyspark
Modes are just the analog of centroids in k-means. A distributed implementation assigns points to clusters in parallel on subsets of the data, then computes new centroids in parallel, and finally collects those results to perform a (weighted) average of those centroids to get the real centroids. Similarly here, after cluster assignment, modes are computed on subsets of the data in parallel, but then they need to be collected and combined into the real new modes. For whatever reason they're calling the 'real' modes 'metamodes'.