用算法聚类结果模拟人工标注，实现无需人工的智能集成聚类。
Crowdsourcing Without People: Modelling Clustering Algorithms as Experts
- 将多个聚类算法输出视为噪声标注，用改进的Dawid-Skene模型融合
- 在真实与模拟数据上均接近最优结果，且极少出现差表现
- 适合不懂数据结构的用户，提升聚类可靠性
本文提出mixsemble，一种将Dawid-Skene模型应用于多模型聚类算法的集成方法。不同于依赖人工标注的传统众包，该框架将聚类算法的输出视为带有噪声的标注。在模拟数据和真实数据集上的实验表明，尽管mixsemble并非总是表现最佳，但其始终能逼近最优结果，避免较差性能。这种稳健性使其在真实数据结构未知时成为实用替代方案，尤其适合非专家用户。
原文摘要 · Abstract (English)
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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