Adaptive Clustering

Adaptive clustering focuses on developing algorithms that automatically adjust their parameters or structure to effectively group data points, even when facing challenges like high dimensionality, noise, or non-uniform data distributions. Current research emphasizes methods incorporating self-supervised learning, reinforcement learning for resource optimization (e.g., in IoT networks), and techniques that leverage pairwise constraints or granular-ball approaches for improved efficiency and robustness. These advancements are significant for improving the accuracy and scalability of clustering in diverse applications, ranging from image analysis and video processing to federated learning and anomaly detection.