What does clustering involve in unsupervised learning?

Clustering is a fundamental technique in unsupervised learning that focuses on grouping data points based on their similarities. This approach allows the algorithm to identify patterns and structures within the data without prior labeling or classification. By evaluating the intrinsic attributes of the data, clustering algorithms place similar items in the same group, or cluster, while maximizing the differences between clusters.

This technique is widely used in various applications, such as market segmentation, image compression, and anomaly detection, where understanding the inherent grouping of data can lead to meaningful insights. Since clustering does not require labeled outcomes, it is particularly valuable for exploratory data analysis, enabling researchers to uncover hidden structures in large datasets.