In this task, you will be presented with a context from an academic paper and a question separated with a
. You have to answer the question based on the context.

For evaluation of clustering performance, we use Calinski-Harabasz score BIBREF42 , also known as the variance ratio criterion. CH score is defined as the ratio between the within-cluster dispersion and the between-cluster dispersion. CH score has a range of $[0, +\infty ]$ and a higher CH score corresponds to a better clustering. For visual validation, we plot and inspect the t-Distributed Stochastic Neighbor Embedding (t-SNE) BIBREF52 and Uniform Manifold Approximation and Projection (UMAP) BIBREF53 mappings of the learned representations as well.
Question: How do they evaluate their method?
Calinski-Harabasz score t-SNE UMAP