# A parameter-free clustering algorithm for missing datasets

arXiv: https://arxiv.org/abs/2404.05363 · HTML: https://mlsanity.com/paper/2404.05363/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 31 references that are in the map.
- First posted on arXiv 8 Apr 2024.

## Builds on (most cited first, 8 of 8)

- [GAIN: Missing Data Imputation using Generative Adversarial Nets](https://mlsanity.com/paper/1806.02920/) (2018)
- [Missing Data Imputation using Optimal Transport](https://mlsanity.com/paper/2002.03860/) (2020)
- [HyperImpute: Generalized Iterative Imputation with Automatic Model Selection](https://mlsanity.com/paper/2206.07769/) (2022)
- [Variational Autoencoder with Arbitrary Conditioning](https://mlsanity.com/paper/1806.02382/) (2018)
- [Transformed Distribution Matching for Missing Value Imputation](https://mlsanity.com/paper/2302.10363/) (2023)
- [k-POD: A Method for k-Means Clustering of Missing Data](https://mlsanity.com/paper/1411.7013/) (2014)
- [Clustering with Missing Features: A Penalized Dissimilarity Measure based approach](https://mlsanity.com/paper/1604.06602/) (2016)
- [Conditional expectation with regularization for missing data imputation](https://mlsanity.com/paper/2302.00911/) (2023)

## Abstract (arXiv)

Missing datasets, in which some objects have missing values in certain dimensions, are prevalent in the Real-world. Existing clustering algorithms for missing datasets first impute the missing values and then perform clustering. However, both the imputation and clustering processes require input parameters. Too many input parameters inevitably increase the difficulty of obtaining accurate clustering results. Although some studies have shown that decision graphs can replace the input parameters of clustering algorithms, current decision graphs require equivalent dimensions among objects and are therefore not suitable for missing datasets. To this end, we propose a Single-Dimensional Clustering algorithm, i.e., SDC. SDC, which removes the imputation process and adapts the decision graph to the missing datasets by splitting dimension and partition intersection fusion, can obtain valid clustering results on the missing datasets without input parameters. Experiments demonstrate that, across three evaluation metrics, SDC outperforms baseline algorithms by at least 13.7%(NMI), 23.8%(ARI), and 8.1%(Purity).