Document Clustering

Clustering groups documents with similar semantic content into categories. Documents within a cluster are more similar to each other than to documents in other clusters.

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Algorithms

• K-means: Creates K spherical clusters. Fast and simple, requires specifying number of clusters.
• Hierarchical: Builds tree of clusters. Good for exploring nested structures.
• DBSCAN: Density-based, finds arbitrarily shaped clusters and identifies outliers.
• HDBSCAN: Hierarchical density-based, automatically determines number of clusters.

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Usage

Choose discovery mode (Automatic, Manual, Method). Select semantic feature space and algorithm. Automatic mode finds optimal cluster count. Use visualizations and quality metrics to evaluate results.

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