{
  "abstract": "Clustering problems increasingly involve complex objects observed over space, such as distributions, matrices, functions, images, or multivariate data, for which a scientifically meaningful dissimilarity between objects is often easier to specify and computationally more tractable than an object-response likelihood model. We propose DISCCO, a Bayesian framework for clustering spatially indexed complex objects using only a pairwise distance matrix and a spatial adjacency graph, with broad applicability and minimal user modeling requirements. The model combines a hierarchical distance-based likelihood accounting for within-cluster compactness and between-cluster separation with a random spatial graph partition prior, ensuring that posterior clusters are spatially contiguous. A key model feature is a set of node-specific frailty parameters that induce dependence among overlapping within-cluster distances and provide posterior summaries of object-level centrality or peripherality within each inferred cluster. We develop a partially collapsed Markov chain Monte Carlo algorithm for posterior inference. Simulations with distribution- and matrix-valued responses show that the proposed spatial distance-clustering framework improves region recovery relative to existing distance-clustering methods, while the frailty layer provides interpretable centrality summaries. Real applications to Houston Census Block Group racial-composition distributions and Western US county-level cancer mortality matrices illustrate how the method recovers interpretable contiguous clusters and frailty-based centrality maps.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2609.34671",
  "author_total": 3,
  "authors": [
    {
      "name": "Srijato Bhattacharyya"
    },
    {
      "name": "Huiyan Sang"
    },
    {
      "name": "Bani Mallick"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/165zdffczjt5x.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2609.34671",
  "fulltext_url": null,
  "id": "165zdffczjt5x",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2609.34671",
  "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",
      "stat.ME"
    ],
    [
      "openalex",
      "1702|Bayesian Methods and Mixture Models"
    ]
  ],
  "openalex": "W7214729695",
  "pdf_url": "https://arxiv.org/pdf/2609.34671",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-28",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/165zdffczjt5x.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:25:26Z",
      "native_id": "2609.34671",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2609.34671"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "arxiv:stat.ME",
      "subfield": "statistics"
    }
  ],
  "title": "DISCCO: Distance-Based Bayesian Spatial Clustering of Complex Objects with Node-Frailty Centrality",
  "title_text": "DISCCO: Distance-Based Bayesian Spatial Clustering of Complex Objects with Node-Frailty Centrality",
  "type": "preprint",
  "updated_at": "2026-10-30T12:25:26Z",
  "url": "https://research.draconitic.com/paper/165zdffczjt5x",
  "venue": "arXiv",
  "versions": []
}