Unsupervised learning is a branch of machine learning that focuses on analyzing and interpreting data without the need for labeled outputs. Unlike supervised learning, where the model is trained on a dataset with known labels, unsupervised learning algorithms identify patterns and structures within the data itself. This approach is particularly useful for discovering hidden relationships and insights in large datasets.

There are two primary types of unsupervised learning algorithms: clustering and association. Clustering algorithms group data points based on their similarities, allowing for the identification of distinct clusters within the dataset. Common clustering methods include K-means and hierarchical clustering. On the other hand, association algorithms focus on finding relationships between variables in the data, often used in market basket analysis to identify items frequently purchased together ²⁴.

Unsupervised learning is widely applied in various fields, including anomaly detection, customer segmentation, and data compression, making it a valuable tool for data scientists and analysts.