研究人员开发了一种新的k-means聚类方法，MNAR-$k$-means，旨在处理具有随机缺失值（MNAR）的数据集。该方法专门解决了数值绝对值越小越可能缺失的情况。所提出的技术约束了插补值，并通过数学解释确保了估计的聚类中心的统计一致性。使用替代最小化算法来优化损失函数，模拟证明了其在改进聚类结果和减少偏差方面的有效性。 AI
影响 这项研究为聚类不完整数据集提供了一种新颖的方法，有可能提高在缺失数据普遍存在的领域的分析准确性。
排序理由 该集群包含一篇关于新机器学习方法的arXiv预印本。
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arXiv:2606.31253v1 Announce Type: cross Abstract: The classical $k$-means clustering, based on distances computed from all data features, cannot be directly applied to incomplete data with missing values. A natural extension of $k$-means to missing data is to involve only the obs…
arXiv:2607.01945v1 Announce Type: new 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 the…
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 …
The classical $k$-means clustering, based on distances computed from all data features, cannot be directly applied to incomplete data with missing values. A natural extension of $k$-means to missing data is to involve only the observed positions in clustering, which is equivalent…