# K-Means Clustering

e-means replaces the k-means centroid update with a mini-batch exponential moving average and uses fitted scaling laws to set batch size and decay directly from the problem configuration, so no hyperparameter sweep is needed. Across embedding datasets it reaches the loss Lloyd k-means attains at 25 epochs in 1.4 to 2.6 epochs, translating to 9.5 to 45.5 times wall-clock speedups over Flash-KMeans while converging to a lower loss. On DINO10B, the exact-assignment version partitions 10 billion vectors into 16 million clusters in 2.5 epochs at a loss below Lloyd at 25 epochs.

e-means replaces the k-means centroid update with a mini-batch exponential moving average and uses fitted scaling laws to set batch size and decay directly from the problem configuration, so no hyperparameter sweep is needed. Across embedding datasets it reaches the loss Lloyd k-means attains at 25 epochs in 1.4 to 2.6 epochs, translating to 9.5 to 45.5 times wall-clock speedups over Flash-KMeans while converging to a lower loss. On DINO10B, the exact-assignment version partitions 10 billion vectors into 16 million clusters in 2.5 epochs at a loss below Lloyd at 25 epochs.

The work proposes a privacy-preserving data-sharing and collaboration framework for digital agriculture that combines Principal Component Analysis (PCA) dimensionality reduction with Laplacian-noise-based local differential privacy (ε-LDP), aggregates farmer data in a sandbox environment, and uses K-Means clustering and nearest-neighbor algorithms to recommend potential collaborators, enabling researchers to train personalized models via federated learning or directly on aggregated privacy-protected data; validated on the Wisconsin Farmer's Market and Crop Recommendation real-world datasets, model accuracy shows an average loss of about 5.33% compared with centralized raw-data training, with robustness against attacks such as membership inference assessed via power analysis.

The work proposes a privacy-preserving data-sharing and collaboration framework for digital agriculture that combines Principal Component Analysis (PCA) dimensionality reduction with Laplacian-noise-based local differential privacy (ε-LDP), aggregates farmer data in a sandbox environment, and uses K-Means clustering and nearest-neighbor algorithms to recommend potential collaborators, enabling researchers to train personalized models via federated learning or directly on aggregated privacy-protected data; validated on the Wisconsin Farmer's Market and Crop Recommendation real-world datasets, model accuracy shows an average loss of about 5.33% compared with centralized raw-data training, with robustness against attacks such as membership inference assessed via power analysis.

Using choroid segmentation as a prototype task on 6,076 OCT B-scans from 80 subjects, this study systematically compared scalar no-reference image quality metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), general-purpose ImageNet-pretrained representations, and retinal foundation models as task-specific quality gates, finding that scalar metrics correlate weakly with segmentation Dice (|r| < 0.20), that general-purpose pretrained representations reach linear-probe ROC-AUC up to about 0.77, that the OCT-specific foundation model RETFound reaches about 0.81, and that only the retinal foundation model embeddings form quality-aligned unsupervised K-Means clusters exceeding a patient-level permutation null.

Using choroid segmentation as a prototype task on 6,076 OCT B-scans from 80 subjects, this study systematically compared scalar no-reference image quality metrics (BRISQUE, NIQE, PIQE, SNR, PSNR), general-purpose ImageNet-pretrained representations, and retinal foundation models as task-specific quality gates, finding that scalar metrics correlate weakly with segmentation Dice (|r| < 0.20), that general-purpose pretrained representations reach linear-probe ROC-AUC up to about 0.77, that the OCT-specific foundation model RETFound reaches about 0.81, and that only the retinal foundation model embeddings form quality-aligned unsupervised K-Means clusters exceeding a patient-level permutation null.