t-SNE and PCA

t-SNE and PCA are dimensionality reduction methods that reduce high-dimensional vectors (typically hundreds or thousands of dimensions) to 2D or 3D, enabling visualization of topical clusters. While PCA (Principal Component Analysis) preserves global relationships and shows overall data structure, t-SNE preserves local relationships, making it superior for cluster visualization by grouping semantically similar points together.

In SEO contexts, these techniques are particularly valuable for understanding how AI sees a client's site. Tools like TensorFlow Projector or Jina Visualizer help reveal which articles cluster together, where content duplicates exist, and which topics sit isolated. For example, after visualizing a law firm's 500 blog titles using t-SNE, the visualization reveals whether inheritance law articles form a tight cluster or scatter randomly. These visuals work great for client presentations—they show complex semantic relationships much clearer than spreadsheets.