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klotz: text clustering*

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1. Clustering Unstructured Text with LLM Embeddings and HDBSCAN

   This article demonstrates how to construct a text clustering pipeline by combining large language model embeddings with HDBSCAN, a density-based algorithm. By transforming raw, unstructured text into semantic mathematical representations, you can automatically discover hidden topics in unlabeled datasets without requiring prior labels.
   
   Key stages of the process include:
   * Generating text embeddings using pre-trained sentence-transformers models
   * Reducing embedding dimensionality with UMAP to prepare data for clustering
   * Applying HDBSCAN to identify clusters and visualize results through scatterplots

   2026-06-24 Tags: text clustering, llm embeddings, sentence transformers, umap, hdbscan, dimensionality reduction, unsupervised learning by klotz

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