Text Clustering

Text Clustering is the unsupervised-learning process of grouping documents so that items in the same cluster share similar topics, tone, or intent while differing from other clusters. It begins by converting raw text into numerical vectors—TF-IDF, Word2Vec, or Transformer embeddings—then applies an algorithm such as K-means, Hierarchical Agglomerative, or DBSCAN to partition the embedding space. Dimensionality-reduction techniques like PCA or UMAP improve speed and visualization. Cluster labels are derived either by keyword extraction from centroid terms or by prompting a large language model (LLM) to name each group. Business uses include customer-review segmentation, news feed organization, and deduplication before Retrieval-Augmented Generation (RAG). Key challenges are choosing the right distance metric, determining cluster count, and handling domain drift. Evaluation relies on silhouette scores, topic coherence, or manual inspection. By revealing latent structure without labeled data, Text Clustering turns noisy corpora into actionable buckets for analytics and downstream AI.