Unsupervised Learning is mainly used for which purpose?

Unsupervised Learning is primarily utilized to identify hidden patterns and structures within data. Unlike supervised learning, where the model is trained on labeled datasets with clear outcomes, unsupervised learning operates without predefined labels. This allows the algorithm to explore the data's intrinsic properties, grouping similar data points or delineating clusters based on their features.

The essence of unsupervised learning lies in its ability to unveil underlying relationships and structures that may not be immediately evident. Techniques such as clustering, dimensionality reduction, and association rule learning are commonly employed in this context. These methods enable data analysts and scientists to gain insights into the data, which can inform further analysis or business strategies.

While other options may relate to aspects of machine learning, they do not accurately capture the primary objective of unsupervised learning. For instance, making predictions relies on known labels and is a hallmark of supervised learning. Similarly, providing specific labels for datasets is contrary to the fundamental principle of unsupervised learning, which purposely avoids predetermined categories to discover hidden arrangements in the data.