Unsupervised Machine Learning

Unsupervised machine learning focuses on extracting patterns and structures from unlabeled data without explicit human guidance, aiming to discover inherent relationships and groupings. Current research emphasizes developing robust algorithms, such as k-means, autoencoders, and variations of graph-based methods like LexRank, to address challenges like the "Clever Hans" effect (where models find spurious correlations) and improve the interpretability of results. This field is crucial for diverse applications, ranging from analyzing complex datasets in astronomy and healthcare to optimizing resource allocation in sustainable development and enhancing the efficiency of various engineering systems.