How do you handle clustering in high-dimensional sparse data like text embeddings?

High-dimensional clustering requires dimensionality reduction or cosine similarity-based clustering.

Sparse high-dimensional data (like TF-IDF or embeddings) makes Euclidean distance unreliable. Instead, cosine similarity or dimensionality reduction techniques like PCA, UMAP, or autoencoders are used. Clustering algorithms are adapted to work in latent space rather than raw feature space.