mlsanityData to 7 Oct 2026

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

arXiv 1411.7013 ↗First posted 25 Nov 2014stat.CO stat.MEcited by 13

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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.

Key facts. Cited by 13 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Nov 2014.

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Citing papers per year: 2021: 3; 2022: 0; 2023: 1; 2024: 5; 2025: 2; 2026: 2.

13 papers.

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