Unsupervised learning is a type of machine learning in which algorithms analyze and identify patterns in data without labeled training examples. It is used for clustering, anomaly detection, and dimensionality reduction, helping organizations discover hidden insights from large datasets.

How Unsupervised Learning Works
Unsupervised learning algorithms explore raw data to group similar data points and recognize patterns. Common techniques include clustering methods such as K-means and hierarchical clustering, which segment data into meaningful groups, and dimensionality reduction methods like Principal Component Analysis (PCA), which simplify large datasets while retaining important features.

Why Unsupervised Learning Matters
Unsupervised learning helps businesses analyze customer behavior, detect fraud, and improve recommendation systems. It enables autonomous decision-making in AI applications, such as personalized marketing, medical diagnosis, and cybersecurity threat detection. As businesses collect more unstructured data, unsupervised learning plays a crucial role in uncovering valuable insights without human intervention.