Seed-Guided Semi-Supervised Clustering by A-Contrario Anomaly Detection

• URL: http://arxiv.org/abs/2606.18833v1
• Date: Wed, 17 Jun 2026 09:08:02 GMT
• Title: Seed-Guided Semi-Supervised Clustering by A-Contrario Anomaly Detection
• Abstract summary: This paper introduces a semi-supervised clustering framework grounded in the statistical duality between grouping principles and anomaly detection.<n>We define a cluster as a maximal subset of data points containing no anomalies relative to a null hypothesis of uniform randomness.<n>We evaluate the method on synthetic and real-world benchmarks, including image and text datasets represented through raw, linear-reduced, and neighbourhood-exclusion embeddings.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: This paper introduces a semi-supervised clustering framework grounded in the statistical duality between grouping principles and anomaly detection. We address the challenge of robust cluster definition in noisy environments -- a task where partitioning algorithms often over-assign outliers and density-based methods remain sensitive to heuristic global parameters. Drawing on \textit{a-contrario} statistical reasoning and Gestalt proximity principles, we define a cluster as a maximal subset of data points containing no anomalies relative to a null hypothesis of uniform randomness. Central to this approach is the Perception algorithm, which utilises a principled expectation-based threshold ($\mathbb{E} < 1$) to identify outliers without manual parameter tuning. By treating clustering as the dual of anomaly detection, we employ an iterative ``clustering-by-exclusion'' mechanism. The algorithm is seed-guided, leveraging minimal user-provided labels to initialise robust cluster medians and form initial groups, which are subsequently expanded by admitting non-anomalous points. This approach naturally isolates fringe points, isolated noise, and emerging unknown clusters. We evaluate the method on synthetic and real-world benchmarks, including image and text datasets represented through raw, linear-reduced, and neighbourhood-preserving embeddings. Results demonstrate that with as few as 10--30 seeds per cluster, the proposed method achieves competitive and often very strong performance under a practical low-tuning benchmarking protocol, while maintaining linear scalability with respect to both observations and dimensionality for a fixed number of seeded clusters and iterations.

Related papers

• Turtle shell clustering: A mixture approach to discriminative clustering with applications to flow cytometry and other data [0.254890465057467]
  Generative approaches to clustering provide information on geometric properties of clusters.<n>Discriminative approaches provide boundaries between clusters.<n>We present a fully unsupervised, probabilistic, and discriminative clustering method.
  arXiv  Detail & Related papers  (2026-04-25T00:33:30Z)
• Weight-Informed Self-Explaining Clustering for Mixed-Type Tabular Data [63.62853416081748]
  WISE is a framework that unifies representation, feature weighting, clustering, and interpretation.<n>It produces faithful, human-interpretable explanations grounded in the same primitives that drive clustering.
  arXiv  Detail & Related papers  (2026-04-07T13:18:31Z)
• Hierarchical Clustering With Confidence [6.479319856992936]
  Agglomerative hierarchical clustering is highly sensitive to small perturbations in the data.<n>We show how randomizing hierarchical clustering can be useful not just for measuring stability but also for designing valid hypothesis testing procedures.
  arXiv  Detail & Related papers  (2025-12-06T18:18:20Z)
• Deep Embedding Clustering Driven by Sample Stability [16.53706617383543]
  We propose a deep embedding clustering algorithm driven by sample stability (DECS) Specifically, we start by constructing the initial feature space with an autoencoder and then learn the cluster-oriented embedding feature constrained by sample stability. The experimental results on five datasets illustrate that the proposed method achieves superior performance compared to state-of-the-art clustering approaches.
  arXiv  Detail & Related papers  (2024-01-29T09:19:49Z)
• Dirichlet Process-based Robust Clustering using the Median-of-Means Estimator [16.774378814288806]
  We propose an efficient and automatic clustering technique by integrating the strengths of model-based and centroid-based methodologies.<n>Our method mitigates the effect of noise on the quality of clustering; while at the same time, estimates the number of clusters.
  arXiv  Detail & Related papers  (2023-11-26T19:01:15Z)
• Revisiting Instance-Optimal Cluster Recovery in the Labeled Stochastic Block Model [85.51611950757643]
  We propose IAC (Instance-Adaptive Clustering), the first algorithm whose performance matches the instance-specific lower bounds both in expectation and with high probability.<n>IAC maintains an overall computational complexity of $ mathcalO(n, textpolylog(n) $, making it scalable and practical for large-scale problems.
  arXiv  Detail & Related papers  (2023-06-18T08:46:06Z)
• A Computational Theory and Semi-Supervised Algorithm for Clustering [0.0]
  Clustering is the obtainment of groupings of data with no anomalies.<n>The kernel of the clustering method is the perception anomaly detection algorithm.<n>A semi-supervised clustering algorithm is presented.
  arXiv  Detail & Related papers  (2023-06-12T09:15:58Z)
• Neighbour Consistency Guided Pseudo-Label Refinement for Unsupervised Person Re-Identification [80.98291772215154]
  Unsupervised person re-identification (ReID) aims at learning discriminative identity features for person retrieval without any annotations. Recent advances accomplish this task by leveraging clustering-based pseudo labels. We propose a Neighbour Consistency guided Pseudo Label Refinement framework.
  arXiv  Detail & Related papers  (2022-11-30T09:39:57Z)
• Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning [146.11600461034746]
  Method for unsupervised meta-learning, CACTUs, is a clustering-based approach with pseudo-labeling. This approach is model-agnostic and can be combined with supervised algorithms to learn from unlabeled data. We prove that the core reason for this is lack of a clustering-friendly property in the embedding space.
  arXiv  Detail & Related papers  (2022-09-27T19:04:36Z)
• Bregman Power k-Means for Clustering Exponential Family Data [11.434503492579477]
  We bridge algorithmic advances to classical work on hard clustering under Bregman divergences. The elegant properties of Bregman divergences allow us to maintain closed form updates in a simple and transparent algorithm. We consider thorough empirical analyses on simulated experiments and a case study on rainfall data, finding that the proposed method outperforms existing peer methods in a variety of non-Gaussian data settings.
  arXiv  Detail & Related papers  (2022-06-22T06:09:54Z)
• Gradient Based Clustering [72.15857783681658]
  We propose a general approach for distance based clustering, using the gradient of the cost function that measures clustering quality. The approach is an iterative two step procedure (alternating between cluster assignment and cluster center updates) and is applicable to a wide range of functions.
  arXiv  Detail & Related papers  (2022-02-01T19:31:15Z)
• Lattice-Based Methods Surpass Sum-of-Squares in Clustering [98.46302040220395]
  Clustering is a fundamental primitive in unsupervised learning. Recent work has established lower bounds against the class of low-degree methods. We show that, perhaps surprisingly, this particular clustering model textitdoes not exhibit a statistical-to-computational gap.
  arXiv  Detail & Related papers  (2021-12-07T18:50:17Z)
• Scalable Hierarchical Agglomerative Clustering [65.66407726145619]
  Existing scalable hierarchical clustering methods sacrifice quality for speed. We present a scalable, agglomerative method for hierarchical clustering that does not sacrifice quality and scales to billions of data points.
  arXiv  Detail & Related papers  (2020-10-22T15:58:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.