VelvetFlow: An engineering pipeline for robust multi-density clustering

Problem. Real-world datasets seldom respect a single density scale: tight blobs, elongated ribbons, and isolated points often coexist. Classical algorithms such as DBSCAN or \textit{k}-means require domain-specific parameter tuning and provide only ad-hoc support for anomaly detection.Solution. We introduce VelvetFlow, an engineering pipeline that turns a set of well-understood building blocks into a cohesive, end-to-end workflow for multi-density clustering \emph{and} principled outlier detection. The pipeline is composed of three reusable stages:(i) \emph{Contextual-density splitting} assigns every point to a high- or low-density partition using a single neighbourhood size k.(ii) \emph{Density-aware clustering} applies a Jaccard-guided \textit{FusedNeighbor}+DBSCAN routine to the sparse partition and HDBSCAN to the dense partition-without introducing new hyper-parameters.(iii) \emph{Scaled-MST verification} re-examines the complete k-NN graph, flags weakly connected components, and validates them with a k-NN gate; this step recovers small remote clusters while filtering genuine anomalies.

Keywords:

multi-density clustering , outlier detection , HDBSCAN , DBSCAN , MST , fused neighbor