Which type of machine learning focuses on discovering patterns in unlabeled data?

The type of machine learning that focuses on discovering patterns in unlabeled data is indeed unsupervised learning. In this approach, algorithms are designed to analyze and interpret data without prior labeling or predefined categories. Unsupervised learning is typically used to identify hidden structures or intrinsic groupings within data, making it particularly effective for clustering similar items or reducing dimensionality through techniques like Principal Component Analysis.

This method is essential for scenarios where labeled data is scarce or expensive to obtain, allowing for insights and patterns to be extracted from datasets that would otherwise remain untapped. Common applications include customer segmentation, anomaly detection, and topic modeling in text analysis. By focusing on the patterns present in the data itself, unsupervised learning enables the discovery of underlying relationships that may not be immediately apparent.