# Interactive Bayesian Hierarchical Clustering

arXiv: https://arxiv.org/abs/1602.03258 · HTML: https://mlsanity.com/paper/1602.03258/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Feb 2016.

## Built on by (most cited first, 9 of 9)

- [Learning-Augmented k-means Clustering](https://mlsanity.com/paper/2110.14094/) (2021)
- [Improved Learning-augmented Algorithms for k-means and k-medians Clustering](https://mlsanity.com/paper/2210.17028/) (2022)
- [Bounded Memory Active Learning through Enriched Queries](https://mlsanity.com/paper/2102.05047/) (2021)
- [Fast, Scalable, Warm-Start Semidefinite Programming with Spectral Bundling and Sketching](https://mlsanity.com/paper/2312.11801/) (2023)
- [Active Learning Polynomial Threshold Functions](https://mlsanity.com/paper/2201.09433/) (2022)
- [Active Learning of Classifiers with Label and Seed Queries](https://mlsanity.com/paper/2209.03996/) (2022)
- [Accelerating data-driven algorithm selection for combinatorial partitioning problems](https://mlsanity.com/paper/2402.14332/) (2024)
- [The Complexity of Finding Local Optima in Contrastive Learning](https://mlsanity.com/paper/2509.16898/) (2025)
- [A Revenue Function for Comparison-Based Hierarchical Clustering](https://mlsanity.com/paper/2211.16459/) (2022)

## Abstract (arXiv)

Clustering is a powerful tool in data analysis, but it is often difficult to find a grouping that aligns with a user's needs. To address this, several methods incorporate constraints obtained from users into clustering algorithms, but unfortunately do not apply to hierarchical clustering. We design an interactive Bayesian algorithm that incorporates user interaction into hierarchical clustering while still utilizing the geometry of the data by sampling a constrained posterior distribution over hierarchies. We also suggest several ways to intelligently query a user. The algorithm, along with the querying schemes, shows promising results on real data.