Which clustering method is described as being good for exploratory research on small files?

The hierarchical clustering method is particularly effective for exploratory research on smaller datasets due to its ability to create a hierarchy of clusters. This method does not require the number of clusters to be predetermined, which is beneficial in exploratory analyses where the goal is to uncover the structure in the data.

Hierarchical clustering generates a tree-like structure, known as a dendrogram, that visually represents the relationships among data points. Analysts can investigate various levels of the hierarchy to determine the most meaningful clusters. This approach allows for flexibility and detailed examination of how individual data points relate to one another and the overall distribution.

Additionally, because hierarchical clustering works well with smaller datasets, it provides clear insights without becoming computationally prohibitive, which is an important consideration in exploratory research.