What is the primary goal of clustering algorithms in machine learning?

The primary goal of clustering algorithms in machine learning is to group similar data points together. Clustering is an unsupervised learning technique that focuses on identifying natural groupings within a dataset based on feature similarity without the need for labeled outcomes. The algorithm analyzes the intrinsic characteristics and distances between data points to form clusters of items that are closely related.

This approach is beneficial in various contexts, such as customer segmentation, anomaly detection, and image compression, where the objective is to identify patterns or structures within unlabelled data. By grouping similar data points together, clustering helps to reveal insights and associations that may not be immediately apparent, allowing for more informed decision-making and analysis in diverse applications.

In contrast, the other options refer to different goals associated with various machine learning tasks. Predicting continuous outcomes is a characteristic of regression tasks, minimizing the loss function pertains to the optimization processes involved in training models, and enhancing feature selection is related to improving the input variables used in predictive modeling. These goals do not align with the fundamental purpose of clustering, which focuses solely on the organization of data into meaningful groups.