What type of learning does clustering typically utilize?

Clustering is a method used in unsupervised learning, which means it operates on data without labeled responses. The main objective of clustering is to group similar data points together based on their features, allowing the algorithm to identify patterns and structures inherent in the data.

In unsupervised learning, the algorithm explores the data to find hidden patterns or intrinsic groupings, rather than relying on pre-existing labels to guide the learning process. For example, in customer segmentation, clustering can help identify distinct groups of customers based on their purchasing behaviors without any prior labeling of customer types.

This contrasts with supervised learning, where the model is trained on labeled data with known outcomes, and reinforcement learning, which involves training models based on feedback from previous actions. Semi-supervised learning falls in between these two, utilizing a small amount of labeled data along with a larger amount of unlabeled data to improve learning accuracy.

Thus, clustering's focus on discovering patterns without prior labeling firmly places it in the realm of unsupervised learning.