Which of the following is relevant to unsupervised learning?

Unsupervised learning is a branch of machine learning where the algorithm is trained on data without labeled responses. The primary goal of unsupervised learning is to explore the inherent structure of the data to identify patterns, groupings, or clusters.

Hierarchical clustering is a key technique within unsupervised learning. It involves creating a hierarchy of clusters, allowing for the analysis of how data points relate to one another based on their features without prior knowledge of any labels. This makes it particularly relevant as it helps to uncover the structure in the data, distinguishing distinct groups based on their similarities.

In contrast, heteroscedasticity refers to situations in regression analysis where the variability of the errors does not remain constant across different levels of an independent variable. While this is a critical concept in supervised learning contexts like linear regression, it does not pertain to unsupervised learning methodologies.

The hierarchical principle, while potentially referencing structures, does not specifically denote a concept within unsupervised learning as defined in the context of clustering techniques.

Therefore, the relevance of hierarchical clustering directly ties it to the principles of unsupervised learning, highlighting the significance of this technique in discovering data patterns autonomously.