This content describes a method for hierarchical clustering that leverages mutual information. It explores how mutual information can be applied to build a robust and effective clustering structure.
AdaGraph is a graph-native clustering algorithm from the Structure-Centric Machine Learning (SC-ML) paradigm, which fundamentally dissolves the curse of dimensionality by replacing geometry-centric computation with topology-based computation. Operating within kNN graph topology, it requires no a priori specification of cluster numbers, handles noise, and scales effectively.