Clustering and Topic Modeling with Embeddings
~40 min read
K-Means, HDBSCAN, and BERTopic for unsupervised discovery of topics and document clusters.
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Key points
- •Always reduce embedding dimensionality with UMAP before clustering — raw 768-1536 dim embeddings produce poor clusters due to the curse of dimensionality
- •HDBSCAN's noise label (-1) is a feature, not a bug — real-world corpora have documents that don't cleanly belong to any topic
- •BERTopic's c-TF-IDF names topics by finding words that appear frequently in a cluster but rarely in other clusters — like TF-IDF where the 'document' is the whole cluster