# A Bibliographic View on Constrained Clustering

arXiv: https://arxiv.org/abs/2209.11125 · HTML: https://mlsanity.com/paper/2209.11125/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 14 of its 186 references that are in the map.
- First posted on arXiv 22 Sep 2022.

## Builds on (most cited first, 14 of 14)

- [Discovering New Intents via Constrained Deep Adaptive Clustering with Cluster Refinement](https://mlsanity.com/paper/1911.08891/) (2019)
- [Learning Discrete Representations via Constrained Clustering for Effective and Efficient Dense Retrieval](https://mlsanity.com/paper/2110.05789/) (2021)
- [A Framework for Deep Constrained Clustering -- Algorithms and Advances](https://mlsanity.com/paper/1901.10061/) (2019)
- [Neural network-based clustering using pairwise constraints](https://mlsanity.com/paper/1511.06321/) (2015)
- [Local algorithms for interactive clustering](https://mlsanity.com/paper/1312.6724/) (2013)
- [Interactive Steering of Hierarchical Clustering](https://mlsanity.com/paper/2009.09618/) (2020)
- [A Classification-Based Approach to Semi-Supervised Clustering with Pairwise Constraints](https://mlsanity.com/paper/2001.06720/) (2020)
- [What Do Citation Counts Measure? An Updated Review of Studies on Citations in Scientific Documents Published between 2006 and 2018](https://mlsanity.com/paper/1906.04588/) (2019)
- [Semi-Supervised Clustering with Neural Networks](https://mlsanity.com/paper/1806.01547/) (2018)
- [A semi-supervised sparse K-Means algorithm](https://mlsanity.com/paper/2003.06973/) (2020)
- [Constrained 1-Spectral Clustering](https://mlsanity.com/paper/1505.06485/) (2015)
- [Clustering With Side Information: From a Probabilistic Model to a Deterministic Algorithm](https://mlsanity.com/paper/1508.06235/) (2015)
- [Clustering with a Reject Option: Interactive Clustering as Bayesian Prior Elicitation](https://mlsanity.com/paper/1606.05896/) (2016)
- [Multi-objective Semi-supervised Clustering for Finding Predictive Clusters](https://mlsanity.com/paper/2201.10764/) (2022)

## Abstract (arXiv)

A keyword search on constrained clustering on Web-of-Science returned just under 3,000 documents. We ran automatic analyses of those, and compiled our own bibliography of 183 papers which we analysed in more detail based on their topic and experimental study, if any. This paper presents general trends of the area and its sub-topics by Pareto analysis, using citation count and year of publication. We list available software and analyse the experimental sections of our reference collection. We found a notable lack of large comparison experiments. Among the topics we reviewed, applications studies were most abundant recently, alongside deep learning, active learning and ensemble learning.