Predictive Clustering

Predictive clustering aims to group data points into clusters that are not only internally homogeneous but also predictive of a target variable, bridging unsupervised and supervised learning. Current research emphasizes improving clustering accuracy and calibration, often employing deep learning architectures like convolutional neural networks, restricted Boltzmann machines, and transformers, sometimes integrated with optimization techniques like mixed-integer linear programming. These advancements are impacting diverse fields, from improving vessel traffic management and express delivery systems to enhancing weather forecasting and accelerating reinforcement learning through more efficient state representation.