# Crowdsourcing Without People: Modelling Clustering Algorithms as Experts

arXiv: https://arxiv.org/abs/2509.25395 · HTML: https://mlsanity.com/paper/2509.25395/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 3 of its 23 references that are in the map.
- First posted on arXiv 29 Sep 2025.

## Builds on (most cited first, 3 of 3)

- [Locally Weighted Ensemble Clustering](https://mlsanity.com/paper/1605.05011/) (2016)
- [A Mixture of Generalized Hyperbolic Distributions](https://mlsanity.com/paper/1305.1036/) (2013)
- [Fast Dawid-Skene: A Fast Vote Aggregation Scheme for Sentiment Classification](https://mlsanity.com/paper/1803.02781/) (2018)

## Abstract (arXiv)

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.