# Crowdsourced correlation clustering with relative distance comparisons

arXiv: https://arxiv.org/abs/1709.08459 · HTML: https://mlsanity.com/paper/1709.08459/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Sep 2017.

## Built on by (most cited first, 5 of 5)

- [How to Design Robust Algorithms using Noisy Comparison Oracle](https://mlsanity.com/paper/2105.05782/) (2021)
- [Generalized partitioned local depth](https://mlsanity.com/paper/2303.10167/) (2023)
- [Elliptical Ordinal Embedding](https://mlsanity.com/paper/2105.10457/) (2021)
- [A Revenue Function for Comparison-Based Hierarchical Clustering](https://mlsanity.com/paper/2211.16459/) (2022)
- [Metric k-clustering using only Weak Comparison Oracles](https://mlsanity.com/paper/2601.19333/) (2026)

## Abstract (arXiv)

Crowdsourced, or human computation based clustering algorithms usually rely on relative distance comparisons, as these are easier to elicit from human workers than absolute distance information. A relative distance comparison is a statement of the form "item A is closer to item B than to item C". However, many existing clustering algorithms that use relative distances are rather complex. They are often based on a two-step approach, where the relative distances are first used to learn either a distance matrix, or an embedding of the items, and then some standard clustering method is applied in a second step. In this paper we argue that it should be possible to compute a clustering directly from relative distance comparisons. Our ideas are built upon existing work on correlation clustering, a well-known non-parametric approach to clustering. The technical contribution of this work is twofold. We first define a novel variant of correlation clustering that is based on relative distance comparisons, and hence suitable for human computation. We go on to show that our new problem is closely related to basic correlation clustering, and use this property to design an approximation algorithm for our problem. As a second contribution, we propose a more practical algorithm, which we empirically compare against existing methods from literature. Experiments with synthetic data suggest that our approach can outperform more complex methods. Also, our method efficiently finds good and intuitive clusterings from real relative distance comparison data.