# Semi-supervised clustering methods

arXiv: https://arxiv.org/abs/1307.0252 · HTML: https://mlsanity.com/paper/1307.0252/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2013.

## Built on by (most cited first, 18 of 18)

- [USB: A Unified Semi-supervised Learning Benchmark for Classification](https://mlsanity.com/paper/2208.07204/) (2022)
- [Deep Clustering: A Comprehensive Survey](https://mlsanity.com/paper/2210.04142/) (2022)
- [P-split formulations: A class of intermediate formulations between big-M and convex hull for disjunctive constraints](https://mlsanity.com/paper/2202.05198/) (2022)
- [Semi-supervised New Event Type Induction and Description via Contrastive Loss-Enforced Batch Attention](https://mlsanity.com/paper/2202.05943/) (2022)
- [Clustering with Non-adaptive Subset Queries](https://mlsanity.com/paper/2409.10908/) (2024)
- [Auditing for Diversity using Representative Examples](https://mlsanity.com/paper/2107.07393/) (2021)
- [When Less is More: On the Value of "Co-training" for Semi-Supervised Software Defect Predictors](https://mlsanity.com/paper/2211.05920/) (2022)
- [CLUBench: A Clustering Benchmark](https://mlsanity.com/paper/2605.29933/) (2026)
- [Deep Multi-View Semi-Supervised Clustering with Sample Pairwise Constraints](https://mlsanity.com/paper/2206.04949/) (2022)
- [SSDBCODI: Semi-Supervised Density-Based Clustering with Outliers Detection Integrated](https://mlsanity.com/paper/2208.05561/) (2022)
- [Deep-seeded Clustering for Emotion Recognition from Wearable Physiological Sensors](https://mlsanity.com/paper/2308.09013/) (2023)
- [Constrained Hierarchical Clustering via Graph Coarsening and Optimal Cuts](https://mlsanity.com/paper/2312.04209/) (2023)
- [Text-Based Product Matching -- Semi-Supervised Clustering Approach](https://mlsanity.com/paper/2402.10091/) (2024)
- [Navigating Uncertainties in Machine Learning for Structural Dynamics: A Comprehensive Survey of Probabilistic and Non-Probabilistic Approaches in Forward and Inverse Problems](https://mlsanity.com/paper/2408.08629/) (2024)
- [K-GBS3FCM -- KNN Graph-Based Safe Semi-Supervised Fuzzy C-Means](https://mlsanity.com/paper/2411.14728/) (2024)
- [Utilizing the Mean Teacher with Supcontrast Loss for Wafer Pattern Recognition](https://mlsanity.com/paper/2411.18533/) (2024)
- [A Graph-Partitioning Based Continuous Optimization Approach to Semi-supervised Clustering Problems](https://mlsanity.com/paper/2503.04447/) (2025)
- [Discriminative Ordering Through Ensemble Consensus](https://mlsanity.com/paper/2505.04464/) (2025)

## Abstract (arXiv)

Cluster analysis methods seek to partition a data set into homogeneous subgroups. It is useful in a wide variety of applications, including document processing and modern genetics. Conventional clustering methods are unsupervised, meaning that there is no outcome variable nor is anything known about the relationship between the observations in the data set. In many situations, however, information about the clusters is available in addition to the values of the features. For example, the cluster labels of some observations may be known, or certain observations may be known to belong to the same cluster. In other cases, one may wish to identify clusters that are associated with a particular outcome variable. This review describes several clustering algorithms (known as "semi-supervised clustering" methods) that can be applied in these situations. The majority of these methods are modifications of the popular k-means clustering method, and several of them will be described in detail. A brief description of some other semi-supervised clustering algorithms is also provided.