<ul><li>Effectively applying the K-means algorithm to clustering tasks with incomplete features remains an important research area.</li><li>Recent work has shown that unifying K-means clustering and imputation into one single objective function yields superior results.</li><li>In this work, a unified K-means algorithm that incorporates Mahalanobis distances is proposed.</li><li>Extensive experiments demonstrate that the proposed algorithm consistently outperforms existing approaches in handling incomplete data.</li></ul>

K-Means Clustering With Incomplete Data with the Use of Mahalanobis Distances

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