arXiv:2607.01945v1 Announce Type: cross Abstract: The classical $k$-means clustering cannot be directly used to incomplete data, and existing $k$-means-based clustering for missing data primarily focus on improving the practical accuracy of clustering, whereas most of them lack theoretical guarantees in the asymptotic sense. In this paper, we investigate the statistical properties of $k$-means clustering in the presence of missing data. We first establish the $\sqrt{n}$-excess risk bound and prove the consistency of the estimated cluster centers under general missing mechanisms. For the Missin
The increasing complexity and volume of real-world datasets often involve missing data, driving a need for robust theoretical guarantees in fundamental AI/ML algorithms.
Establishing theoretical guarantees for k-means clustering with missing data improves the reliability and interpretability of clustering results, which is crucial for high-stakes applications in various industries.
The ability to formally understand the statistical properties of k-means clustering with incomplete data reduces uncertainty in model outcomes, enabling more confident deployment in scenarios where data completeness cannot be assumed.
- · AI/ML researchers
- · Data scientists
- · Industries with incomplete datasets (e.g., healthcare, finance)
- · Algorithm developers
- · Systems highly reliant on imputation methods lacking theoretical backing
Improved accuracy and reliability of clustering results in practical applications featuring missing data.
Accelerated development and adoption of clustering algorithms in fields historically constrained by data incompleteness.
Enhanced trust in AI systems that make data-driven decisions from imperfect datasets, potentially broadening AI's application scope.
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