What is text clustering? A complete guide

This post details text clustering as a method to organize unstructured data by grouping similar documents based on meaning. It covers the stages of text clustering: preprocessing, embedding (using TF-IDF, Word2Vec, BERT), and clustering algorithms (K-means, DBSCAN, hierarchical clustering). It highlights benefits like automated organization, discovery of hidden patterns, and better search results. A Python workflow is provided for building, visualizing, and evaluating clusters, with integration into Meilisearch for searchable clusters. Challenges such as high dimensionality, ambiguous language, and scaling are discussed, along with common mistakes like neglecting preprocessing and evaluation.