A Robust and High-Efficiency Active Clustering Framework with Multi-User Collaboration

Active constraint-based clustering enhances semi-supervised clustering through a machine-led interaction process. This approach dynamically selects the most informative constraints to query, minimizing the number of human annotations required. Existing methods face three key challenges in real-world applications: scalability, timeliness, and robustness against user annotation errors. In this work, we propose a robust and high-efficiency Active Clustering framework with Multi-user Collaboration (ACMC). ACMC constructs a diffusion tree using the nearest-neighbor technique and employs a multi-user online collaboration framework to iteratively refine clustering results. In each iteration: (a) nodes with high uncertainty and representativeness are selected in batch; (b) well-designed multi-user asynchronous query categorizes selected nodes using neighborhood sets, reducing individual workloads and improving overall timeliness; (c) user-provided constraints and newly discovered categories are synchronized, with user confidences dynamically updated to enhance robustness against erroneous annotations; (d) categorized nodes, stored in neighborhood sets, serve as sources in the diffusion tree to refine the clusters. Experimental results demonstrate that ACMC outperforms baseline methods in terms of clustering quality, scalability, and robustness against user annotation errors.

• Publication year

  2025

• Venue

  International Conference on Information and Knowledge Management

• Publication date

  2025-11-10

• Fields of study

  Computer Science

• Identifiers
  DOI 10.1145/3746252.3761165
• External record

  Open on Semantic Scholar

• Source metadata

  Semantic Scholar

• No claims are published for this paper.

• No concepts are published for this paper.

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