What is the relationship between clustering methods and unsupervised learning?

Clustering is indeed a technique used in unsupervised learning, which is a branch of machine learning where the model is trained on data that does not have labeled outcomes. In unsupervised learning, the goal is to uncover hidden patterns or intrinsic structures in data. Clustering specifically involves grouping a set of objects in such a way that objects in the same group (or cluster) are more similar to each other than to those in other groups.

This is in contrast to supervised learning, where the model learns from labeled data, meaning each training sample comes with a corresponding output label. Since clustering does not rely on pre-defined labels, it effectively handles situations where the underlying structure of the data needs to be explored without external guidance.

Understanding the role of clustering within the context of unsupervised learning is critical as it highlights how machines can identify patterns and make sense of unstructured data, which is increasingly important in various applications such as customer segmentation, anomaly detection, and more.