What is the primary use of clustering in machine learning?

The primary use of clustering in machine learning is to discover patterns within a dataset. Clustering is an unsupervised learning technique that groups data points based on their similarities without needing pre-assigned labels. By identifying groups, or clusters, within the data, clustering algorithms help to reveal underlying structures, trends, and patterns that might not be apparent through other means. This is particularly useful in exploratory data analysis, marketing segmentation, or identifying anomalies in datasets, where the goal is to understand the distribution and relationships among the data rather than to predict specific outcomes or classify instances.

Other alternatives focus on different tasks in machine learning. Assigning specific labels pertains to supervised learning methods like classification, while reducing overfitting generally involves strategies during model training to improve generalization. Predicting continuous values is a characteristic of regression tasks. Thus, clustering stands out primarily as a method for pattern discovery.