What kind of algorithms does unsupervised machine learning typically use?

Unsupervised machine learning typically employs algorithms that do not require labeled data. This approach is fundamentally different from supervised learning, where models are trained on datasets that include input-output pairs, meaning that each data point is associated with a specific label or target outcome. In unsupervised learning, the goal is to identify patterns, themes, or intrinsic structures within the data itself, without any prior labels.

Common techniques in unsupervised learning include clustering, where data points are grouped based on similarity, and dimensionality reduction, which helps to simplify datasets while preserving essential relationships. Examples include algorithms like K-means clustering and Principal Component Analysis (PCA). By focusing on the inherent structure of the data, unsupervised algorithms help to reveal hidden insights that can inform further analysis and decision-making, making them essential tools in the realm of data exploration and pattern recognition.