Crowdsourcing Without People: Modelling Clustering Algorithms as Experts

• URL: http://arxiv.org/abs/2509.25395v1
• Date: Mon, 29 Sep 2025 18:52:37 GMT
• Title: Crowdsourcing Without People: Modelling Clustering Algorithms as Experts
• Authors: Jordyn E. A. Lorentz, Katharine M. Clark,
• Abstract summary: mixsemble is an ensemble method that adapts the Dawid-Skene model to aggregate predictions from multiple model-based clustering algorithms.<n>Unlike traditional crowdsourcing, which relies on human labels, the framework models the outputs of clustering algorithms as noisy annotations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces mixsemble, an ensemble method that adapts the Dawid-Skene model to aggregate predictions from multiple model-based clustering algorithms. Unlike traditional crowdsourcing, which relies on human labels, the framework models the outputs of clustering algorithms as noisy annotations. Experiments on both simulated and real-world datasets show that, although the mixsemble is not always the single top performer, it consistently approaches the best result and avoids poor outcomes. This robustness makes it a practical alternative when the true data structure is unknown, especially for non-expert users.

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