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VelvetFlow: An engineering pipeline for robust multi-density clustering | ||
| Journal of Discrete Mathematics and Its Applications | ||
| مقاله 3، دوره 10، شماره 4، اسفند 2025، صفحه 333-358 اصل مقاله (3.42 M) | ||
| نوع مقاله: Full Length Article | ||
| شناسه دیجیتال (DOI): 10.22061/jdma.2025.12039.1131 | ||
| نویسندگان | ||
| Hossein Eyvazi* ؛ Mohammad Badzohreh؛ Seyed Ali Shahrokhi | ||
| Department of Computer Science, University of Tarbiat Modares, Tehran, I. R. Iran | ||
| تاریخ دریافت: 20 اردیبهشت 1404، تاریخ بازنگری: 15 تیر 1404، تاریخ پذیرش: 18 مرداد 1404 | ||
| چکیده | ||
| 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. | ||
| کلیدواژهها | ||
| multi-density clustering؛ outlier detection؛ HDBSCAN؛ DBSCAN؛ MST؛ fused neighbor | ||
| مراجع | ||
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