Which of the following describes unsupervised learning?

Unsupervised learning is a type of machine learning that focuses on identifying patterns in data without the use of predefined labels. In unsupervised learning, algorithms analyze the input data to discover hidden structures or groupings. This is particularly useful in situations where the data lacks explicit categories or classifications, allowing the system to autonomously determine relationships within the dataset.

In contrast, the other options describe different aspects of machine learning. For instance, using labeled data to classify information refers to supervised learning, where the model is trained on a dataset that includes input-output pairs. The notion of improving existing models aligns with reinforcement learning or iterative refinement processes. Finally, learning that involves human guidance pertains to supervised learning or interactive machine learning, where human input is necessary to help shape the learning process. Each of these describes approaches that differ fundamentally from the nature and processes of unsupervised learning.