What type of machine learning algorithms extracts trends from large data sets?

Unsupervised machine learning algorithms are designed to identify patterns and extract trends from data without prior labeling or categorization. This type of learning is particularly useful when dealing with large datasets where the relationships and structures are not immediately apparent. By analyzing the data on its own, unsupervised learning can uncover hidden patterns, group similar data points, and provide insights that allow for better understanding of the underlying data structure.

Techniques commonly used in unsupervised learning include clustering (such as k-means) and dimensionality reduction (such as principal component analysis). These approaches enable the extraction of meaningful information from complex datasets, making them well-suited for analyzing trends and relationships that would otherwise remain unnoticed if supervised labels were introduced.