EVINGCA: Adaptive Graph Clustering with Evolving Neighborhood Statistics

• URL: http://arxiv.org/abs/2511.00064v2
• Date: Wed, 05 Nov 2025 07:06:55 GMT
• Title: EVINGCA: Adaptive Graph Clustering with Evolving Neighborhood Statistics
• Authors: Randolph Wiredu-Aidoo,
• Abstract summary: Clustering algorithms often rely on restrictive assumptions.<n>EVINGCA is a graph-based clustering algorithm that treats cluster formation as an adaptive non-linear graph evolving process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Clustering algorithms often rely on restrictive assumptions: K-Means and Gaussian Mixtures presuppose convex, Gaussian-like clusters, while DBSCAN and HDBSCAN capture non-convexity but can be highly sensitive. I introduce EVINGCA (Evolving Variance-Informed Nonparametric Graph Construction Algorithm), a density-variance based clustering algorithm that treats cluster formation as an adaptive, evolving process on a nearest-neighbor graph. EVINGCA expands rooted graphs via breadth-first search, guided by continuously updated local distance and shape statistics, replacing fixed density thresholds with local statistical feedback. With spatial indexing, EVINGCA features log-linear complexity in the average case and exhibits competitive performance against baselines across a variety of synthetic, real-world, low-d, and high-d datasets.

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