--- title: Clustering Unstructured Text with LLM Embeddings and HDBSCAN description: A step-by-step guide to building a text clustering pipeline using LLM embeddings and HDBSCAN. The workflow covers generating embeddings with the... canonical: https://daily.dev/posts/clustering-unstructured-text-with-llm-embeddings-and-hdbscan-cihptxhvk twitter:card: summary_large_image twitter:site: @dailydotdev og:type: website og:site_name: daily.dev og:title: Clustering Unstructured Text with LLM Embeddings and HDBSCAN | daily.dev og:description: A step-by-step guide to building a text clustering pipeline using LLM embeddings and HDBSCAN. The workflow covers generating embeddings with the... og:url: https://daily.dev/posts/clustering-unstructured-text-with-llm-embeddings-and-hdbscan-cihptxhvk og:image: https://api.daily.dev/og/posts/CihpTxHvK.png og:image:alt: Clustering Unstructured Text with LLM Embeddings and HDBSCAN og:image:width: 1200 og:image:height: 630 og:locale: en --- > ## Documentation Index > Fetch the complete documentation index at: https://daily.dev/llms.txt > Use this file to discover all available pages before exploring further. # Clustering Unstructured Text with LLM Embeddings and HDBSCAN **[Machine Learning Mastery](https://daily.dev/sources/mlm)** · 4 min read · 0 upvotes · 0 comments ## Summary A step-by-step guide to building a text clustering pipeline using LLM embeddings and HDBSCAN. The workflow covers generating embeddings with the all-MiniLM-L6-v2 sentence-transformers model from Hugging Face, reducing dimensionality to 5 components using UMAP, and applying HDBSCAN to automatically discover topic clusters in unlabeled text. The 20 Newsgroups dataset is used as a practical example, with 150 sampled articles clustered without using their labels. Key advantages highlighted include semantic richness of embeddings and HDBSCAN's ability to auto-determine cluster count and detect outliers. ## Full article daily.dev links to this article rather than hosting it. 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