Clustering is an unsupervised machine learning technique that aims to group similar data points into clusters based on their features. Unlike supervised learning, clustering does not rely on labeled data; instead, it identifies patterns and structures within the data itself. Various algorithms, such as k-means and DBSCAN, are employed to perform clustering, each with its own approach to defining and finding clusters. This technique is widely used in exploratory data analysis, customer segmentation, and pattern recognition, allowing researchers and analysts to uncover insights and relationships within complex datasets. Evaluating the quality of clusters is essential, as there is no ground truth to verify against.