Which of the following correctly defines clustering in machine learning?

Clustering in machine learning is defined as the process of grouping data points based on similarity. This technique allows the algorithm to identify patterns and relationships within the data without any predefined labels. In clustering, data points that share similar characteristics are grouped together in clusters, making it easier to analyze and understand the structure of the data. It is an unsupervised learning method, meaning that the model learns the structure of the data without labeled training examples.

This definition highlights the essence of clustering, which is to find inherent relationships in the data rather than assigning specific output labels or predicting outcomes. The other options, while relevant to different types of machine learning tasks, do not accurately describe clustering. Assigning labels pertains to supervised learning methods, while predicting outcomes is focused on regression or classification tasks. The choice that correctly defines clustering is indeed the grouping of data points based on their similarities.