Hierarchical Clustering

Hierarchical clustering is an unsupervised machine learning technique aiming to organize data into a nested hierarchy of clusters, revealing both fine-grained and high-level groupings. Current research emphasizes developing efficient algorithms, such as those based on agglomerative methods, stochastic blockmodels, and graph-based approaches, to handle high-dimensional data and address challenges like fairness and scalability. These advancements improve the interpretability and accuracy of cluster analysis across diverse fields, including genomics, image processing, and network analysis, leading to better insights and more effective decision-making.