What does unsupervised learning typically aim to achieve?

Unsupervised learning primarily focuses on identifying hidden patterns and structures in datasets that do not have labeled outputs. In this learning paradigm, the algorithm processes the input data without any guidance regarding the expected outcomes, allowing it to discover inherent groupings or clusters among the data points. For instance, techniques such as clustering and dimensionality reduction are common in unsupervised learning, where the aim is to reveal insights that were not previously known.

The approach differs significantly from supervised learning, which relies on labeled data for training and is designed to make predictions or classifications based on that labeled input. In unsupervised learning, since there is no predefined output to guide the algorithm, the primary objective is exploratory analysis rather than prediction or classification.

Because of this distinctive focus on unlabeled data and the discovery of patterns within, finding hidden patterns is the core aim of unsupervised learning.