Unsupervised Clustering

Unsupervised clustering studies how to divide unlabeled data into meaningful groups, supporting tasks such as scientific discovery, data exploration, and anomaly analysis. Methods range from k-means and hierarchical clustering to density-based and spectral approaches, with newer work learning representations, feature weights, or shared structure across views and time. Key challenges include choosing the number of clusters, handling noise, missing data, and unequal cluster shapes, and making results stable, interpretable, and scalable. Deep methods and language-model-assisted approaches can help in some settings, but benchmarks show that conventional algorithms often remain competitive, so method choice and reliable evaluation depend strongly on the data and task.