Unsupervised learning is a machine learning approach where algorithms are trained on unlabeled data to identify patterns and structures without predefined labels. Techniques such as clustering, dimensionality reduction, and anomaly detection are commonly used in this context. This method is especially useful for exploratory data analysis, allowing researchers and data scientists to gain insights and discover hidden relationships within the data, particularly when labeled data is scarce or unavailable.
