clustering

What problem does it solve?

Clustering is essential to automatically group similar items, reveal hidden patterns, and organize data without predefined labels, reducing manual tagging and exploration time.

Core Features & Use Cases

• Unsupervised organization: Group documents, products, or user data into coherent clusters based on semantic similarity.
• Topic discovery and naming: Assign human-readable labels to clusters to enable quick understanding of topics.
• Exploratory data analysis: Compare cluster compositions, identify outliers, and guide downstream workflows like segmentation or recommendation.

Quick Start

Provide a list of items to cluster (texts, product descriptions, or user content), choose an embedding model and a clustering method (KMeans/DBSCAN/HDBSCAN), and optionally set the number of clusters. Example: cluster_documents(items, method='kmeans', n_clusters=3) to generate three topics and inspect their contents.