EVINGCA: Adaptive Graph Clustering with Evolving Neighborhood Statistics

Clustering is a fundamental tool for discovering structure in data, yet many existing methods rely on restrictive assumptions. Algorithms such as K-Means and Gaussian Mixtures favor convex or Gaussian clusters, while density-based approaches like DBSCAN and HDBSCAN struggle with variable densities or moderate dimensionality. This paper introduces EVINGCA (Evolving Variance-Informed Nonparametric Graph Construction Algorithm), a density-variance-based clustering method that grows clusters incrementally using breadth-first search on a nearest-neighbor graph. Edges are filtered via z-scores of neighbor distances, with estimates refined as clusters expand, enabling adaptation to cluster-specific structure, and a recovery regime distinct from that of existing alternatives. Over-segmentation is exploited by a propagation phase, which propagates inner, denser "skeletons" out to sharp decision boundaries in low-contrast regions. Experiments on 28 diverse datasets demonstrate competitive runtime behavior and a statistically significant improvement over baseline methods in ARI-based label recovery capacity.