Definition
Unsupervised learning is a type of machine learning where algorithms learn patterns from data without explicit guidance or predefined labels. The system explores the data to discover hidden structures, relationships, or groupings on its own. This process is crucial for identifying anomalies, segmenting customers, or reducing data dimensionality in complex datasets. For instance, an unsupervised learning model can analyze network traffic to detect unusual patterns indicative of a cyberattack. Unlike supervised learning, which requires labeled examples, unsupervised learning works with raw, unlabeled information. This concept is frequently tested in cybersecurity certifications like CompTIA CySA+ and cloud security domains.