{
  "abstract": "We introduce Round-Trip KNN Clustering (RTKNNC), a graph-based method for finding cluster structure at several neighbourhood scales without requiring the number of clusters in advance. Unlike approaches that first make a $k$-nearest-neighbour (KNN) graph undirected, RTKNNC keeps both directions of the neighbour relation: which points a given point selects and which points select it. Incoming selections are treated as weighted votes that help decide which local connections remain visible during a recursive forward-and-reverse traversal. Repeating the procedure for increasing $K$ reveals how groups persist or merge as the neighbourhood scale grows; for the reference inverse-square model before structural refinement, clusters can merge but do not split. Because graph connectivity can occasionally join distinct groups through a sparse bridge or a small region of overlap, we add an optional label-free refinement. It first tests whether an already formed component is better described by two or three Gaussian subpopulations, and accepts a subdivision only when the proposed groups are large enough and consistent with the visible KNN graph. Across eight synthetic datasets and $K=2,\\ldots,16$, independent C and Python implementations produced identical partitions in all 120 reference runs. Refinement increased adjusted Rand index from $0.7817$ to $0.9627$ on a variable-density benchmark and from $0.8083$ to $0.9853$ on a sparse-bridge benchmark. Comparisons with seven external clustering methods show competitive performance while preserving a label-free cluster-construction process.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.06795",
  "author_total": 3,
  "authors": [
    {
      "name": "Eraldo Pereira Marinho"
    },
    {
      "name": "Caetano Mazzoni Ranieri"
    },
    {
      "name": "Fabricio Aparecido Breve"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/7brfs36a31sb9.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.06795",
  "fulltext_url": null,
  "id": "7brfs36a31sb9",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.06795",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "cs.LG"
    ],
    [
      "arxiv",
      "stat.ML"
    ],
    [
      "openalex",
      "1702|Advanced Clustering Algorithms Research"
    ]
  ],
  "openalex": "W7220693248",
  "pdf_url": "https://arxiv.org/pdf/2610.06795",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-05",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/7brfs36a31sb9.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:27:16Z",
      "native_id": "2610.06795",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.06795"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ML",
      "subfield": "statistics"
    }
  ],
  "title": "Round-Trip KNN Clustering: multiscale hierarchical cluster detection on directed nearest-neighbour graphs",
  "title_text": "Round-Trip KNN Clustering: multiscale hierarchical cluster detection on directed nearest-neighbour graphs",
  "type": "preprint",
  "updated_at": "2026-10-30T12:27:16Z",
  "url": "https://research.draconitic.com/paper/7brfs36a31sb9",
  "venue": "arXiv",
  "versions": []
}