Interpretable hierarchical clustering

Abstract

There is a need for more effective and efficient hierarchical clustering. This need can be addressed by, for example, solutions for performing interpretable hierarchical clustering. In one example, a method includes performing a group of hierarchical clustering routines to generate a group of final hierarchical clusters; for each cluster-feature pair of a group of cluster-feature pairs, determining an intra-cluster variable importance measure, an incremental representation measure, and an overall variable importance based at least in part on the intra-cluster variable importance measure the incremental representation measure; generating predicted explanatory metadata for the group of hierarchical clustering routines based at least in part on each overall variable importance measure for each cluster-feature pair of the group of cluster-feature pairs; and performing one or more prediction-based actions based at least in part on the predicted explanatory metadata.