Clustering is an unsupervised learning technique in machine learning that aims to group data points into clusters based on their similarities. The primary goal is to identify natural groupings within a dataset, where data points in the same cluster exhibit high similarity, while those in different clusters are distinct. This technique is widely used in various applications, such as customer segmentation in marketing, image recognition, and anomaly detection. Different algorithms, such as K-means, DBSCAN, and hierarchical clustering, can be employed to achieve effective clustering, each with its own strengths and weaknesses depending on the data characteristics and desired outcomes.