# k-POD: A Method for k-Means Clustering of Missing Data

arXiv: https://arxiv.org/abs/1411.7013 · HTML: https://mlsanity.com/paper/1411.7013/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 25 Nov 2014.

## Built on by (most cited first, 13 of 13)

- [Clustering multilayer graphs with missing nodes](https://mlsanity.com/paper/2103.03235/) (2021)
- [Model-based Clustering with Missing Not At Random Data](https://mlsanity.com/paper/2112.10425/) (2021)
- [Statistical Jump Model for Mixed-Type Data with Missing Data Imputation](https://mlsanity.com/paper/2409.01208/) (2024)
- [Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring](https://mlsanity.com/paper/2411.09726/) (2024)
- [The geometry of financial institutions -- Wasserstein clustering of financial data](https://mlsanity.com/paper/2305.03565/) (2023)
- [Fast model-based clustering of partial records](https://mlsanity.com/paper/2103.16336/) (2021)
- [A parameter-free clustering algorithm for missing datasets](https://mlsanity.com/paper/2404.05363/) (2024)
- [Using ChatGPT for Data Science Analyses](https://mlsanity.com/paper/2404.08480/) (2024)
- [Cluster Quilting: Spectral Clustering for Patchwork Learning](https://mlsanity.com/paper/2406.13833/) (2024)
- [Distributed clustering in partially overlapping feature spaces](https://mlsanity.com/paper/2510.09799/) (2025)
- [An Interdisciplinary and Cross-Task Review on Missing Data Imputation](https://mlsanity.com/paper/2511.01196/) (2025)
- [MNAR-k-means: A k-means Clustering for Data Missing Not at Random with Magnitude-Decaying Probability](https://mlsanity.com/paper/2606.31253/) (2026)
- [Statistical Properties of k-means Clustering for Data Missing Completely at Random](https://mlsanity.com/paper/2607.01945/) (2026)

## Abstract (arXiv)

The k-means algorithm is often used in clustering applications but its usage requires a complete data matrix. Missing data, however, is common in many applications. Mainstream approaches to clustering missing data reduce the missing data problem to a complete data formulation through either deletion or imputation but these solutions may incur significant costs. Our k-POD method presents a simple extension of k-means clustering for missing data that works even when the missingness mechanism is unknown, when external information is unavailable, and when there is significant missingness in the data.