# Relaxed Oracles for Semi-Supervised Clustering

arXiv: https://arxiv.org/abs/1711.07433 · HTML: https://mlsanity.com/paper/1711.07433/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Nov 2017.

## Built on by (most cited first, 4 of 4)

- [Learning-Augmented k-means Clustering](https://mlsanity.com/paper/2110.14094/) (2021)
- [How to Design Robust Algorithms using Noisy Comparison Oracle](https://mlsanity.com/paper/2105.05782/) (2021)
- [DMS: Differentiable Mean Shift for Dataset Agnostic Task Specific Clustering Using Side Information](https://mlsanity.com/paper/2305.18492/) (2023)
- [Metric k-clustering using only Weak Comparison Oracles](https://mlsanity.com/paper/2601.19333/) (2026)

## Abstract (arXiv)

Pairwise "same-cluster" queries are one of the most widely used forms of supervision in semi-supervised clustering. However, it is impractical to ask human oracles to answer every query correctly. In this paper, we study the influence of allowing "not-sure" answers from a weak oracle and propose an effective algorithm to handle such uncertainties in query responses. Two realistic weak oracle models are considered where ambiguity in answering depends on the distance between two points. We show that a small query complexity is adequate for effective clustering with high probability by providing better pairs to the weak oracle. Experimental results on synthetic and real data show the effectiveness of our approach in overcoming supervision uncertainties and yielding high quality clusters.