Mean Shift

Mean shift is an iterative clustering algorithm that identifies data density modes by iteratively moving data points towards regions of higher density. Current research focuses on improving its efficiency, particularly through stochastic variations and GPU acceleration, as well as enhancing its performance in generalized category discovery and outlier detection by integrating it with contrastive learning and autoencoders. These advancements are significant for various applications, including image processing, pattern recognition, and representation learning, offering improved accuracy and scalability for unsupervised clustering tasks.