Which method in machine learning divides data into clusters?

Clustering is a method in machine learning specifically designed to group similar data points together, thereby dividing the dataset into distinct clusters. This technique is particularly useful in exploratory data analysis, as it allows you to identify natural groupings in your data without any prior labels or annotated classes.

Clustering algorithms, such as K-means and hierarchical clustering, aim to minimize intra-cluster variability while maximizing inter-cluster variability. This means that the objective is to have items within the same cluster be as similar as possible, while items in different clusters are as dissimilar as possible. This approach is commonly used in tasks such as customer segmentation, image analysis, and organizing computing clusters.

In contrast, classification assigns predefined labels to data points, regression predicts continuous outcomes, and supervised learning encompasses techniques that rely on labeled datasets. These methods do not focus on dividing data into clusters in the same manner as clustering does. This distinction underscores why clustering is the correct answer concerning dividing data into groups based on similarities.