Correlation Clustering

Correlation clustering aims to partition data points into clusters that minimize disagreements with pairwise similarity or dissimilarity information, often represented as a signed graph. Current research focuses on developing efficient algorithms, particularly for large-scale and dynamic data, exploring variations like multilayer correlation clustering and fair correlation clustering to address biases and real-world constraints, and improving approximation guarantees for existing algorithms. These advancements have significant implications for various applications, including image analysis (e.g., organoid images, bird sounds), social network analysis, and machine learning tasks where efficient and fair clustering is crucial.