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.

One of the primary techniques used in unsupervised learning is clustering, which groups data points based on their similarities. Common clustering algorithms include K-means, hierarchical clustering, and DBSCAN. These methods help in segmenting data into distinct clusters, making it easier to analyze and understand the underlying patterns.

Unsupervised learning is widely applied in various fields, including anomaly detection, market segmentation, and recommendation systems. It allows for the exploration of data without prior knowledge, making it a powerful tool for data analysis and interpretation ¹².