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The paper proposes DK-GBMKKM, a dynamic kernel-space granular-ball multiple kernel k-means clustering method that adapts to fused-kernel geometry for improved performance on various datasets.
This paper proposes an adaptive and efficient KNN classifier via granular-ball computing that dynamically determines the k value using granular ball neighborhoods, improving accuracy and robustness while reducing computational cost. The method is open-sourced on GitHub.