k-POD: A Method for k-Means Clustering of Missing Data
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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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- 2021Clustering multilayer graphs with missing nodescited by 2
- 2021Model-based Clustering with Missing Not At Random Datacited by 2
- 2024Statistical Jump Model for Mixed-Type Data with Missing Data Imputationcited by 2
- 2024Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoringcited by 2
- 2023The geometry of financial institutions -- Wasserstein clustering of financial datacited by 1
- 2021Fast model-based clustering of partial recordscited by 0
- 2024A parameter-free clustering algorithm for missing datasetscited by 0
- 2024Using ChatGPT for Data Science Analysescited by 0
- 2024Cluster Quilting: Spectral Clustering for Patchwork Learningcited by 0
- 2025Distributed clustering in partially overlapping feature spacescited by 0
- 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0
- 2026MNAR-k-means: A k-means Clustering for Data Missing Not at Random with Magnitude-Decaying Probabilitycited by 0
- 2026Statistical Properties of k-means Clustering for Data Missing Completely at Random2 Jul 2026
- 2026MNAR-k-means: A k-means Clustering for Data Missing Not at Random with Magnitude-Decaying Probability30 Jun 2026
- 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputation3 Nov 2025
- 2025Distributed clustering in partially overlapping feature spaces10 Oct 2025
- 2024Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring14 Nov 2024
- 2024Statistical Jump Model for Mixed-Type Data with Missing Data Imputation2 Sep 2024
- 2024Cluster Quilting: Spectral Clustering for Patchwork Learning19 Jun 2024
- 2024Using ChatGPT for Data Science Analyses12 Apr 2024
- 2024A parameter-free clustering algorithm for missing datasets8 Apr 2024
- 2023The geometry of financial institutions -- Wasserstein clustering of financial data5 May 2023
- 2021Model-based Clustering with Missing Not At Random Data20 Dec 2021
- 2021Fast model-based clustering of partial records30 Mar 2021
1 more
- 2021Clustering multilayer graphs with missing nodes4 Mar 2021
- 2021Clustering multilayer graphs with missing nodescited by 2
- 2021Model-based Clustering with Missing Not At Random Datacited by 2
- 2024Statistical Jump Model for Mixed-Type Data with Missing Data Imputationcited by 2
- 2024Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoringcited by 2
- 2023The geometry of financial institutions -- Wasserstein clustering of financial datacited by 1
- 2021Fast model-based clustering of partial recordscited by 0
- 2024A parameter-free clustering algorithm for missing datasetscited by 0
- 2024Using ChatGPT for Data Science Analysescited by 0
- 2024Cluster Quilting: Spectral Clustering for Patchwork Learningcited by 0
- 2025Distributed clustering in partially overlapping feature spacescited by 0
- 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputationcited by 0
- 2026MNAR-k-means: A k-means Clustering for Data Missing Not at Random with Magnitude-Decaying Probabilitycited by 0
12 most cited of 13 All 13 (JSON)
- 2026Statistical Properties of k-means Clustering for Data Missing Completely at Random2 Jul 2026
- 2026MNAR-k-means: A k-means Clustering for Data Missing Not at Random with Magnitude-Decaying Probability30 Jun 2026
- 2025An Interdisciplinary and Cross-Task Review on Missing Data Imputation3 Nov 2025
- 2025Distributed clustering in partially overlapping feature spaces10 Oct 2025
- 2024Spatio-Temporal Jump Model for Urban Thermal Comfort Monitoring14 Nov 2024
- 2024Statistical Jump Model for Mixed-Type Data with Missing Data Imputation2 Sep 2024
- 2024Cluster Quilting: Spectral Clustering for Patchwork Learning19 Jun 2024
- 2024Using ChatGPT for Data Science Analyses12 Apr 2024
- 2024A parameter-free clustering algorithm for missing datasets8 Apr 2024
- 2023The geometry of financial institutions -- Wasserstein clustering of financial data5 May 2023
- 2021Model-based Clustering with Missing Not At Random Data20 Dec 2021
- 2021Fast model-based clustering of partial records30 Mar 2021
12 newest of 13 All 13 (JSON)