Semantic Clustering

Semantic clustering is the process of grouping keywords into thematic clusters based on embeddings and algorithms such as K-means. Each cluster becomes a potential content unit — an article or page in the site architecture.

Unlike LLM clustering, which is expensive and non-deterministic, the embedding method delivers repeatable results with 99%+ token savings. The clustering pipeline consists of 6 steps: keyword expansion tool (500+ phrases from one seed), phrase clustering (embeddings + K-means), cluster naming (Central Entity per cluster), cluster validation (SERP Overlap), topical mapping (CORE/OUTER), and content gap detection (missing topics). The result is a complete topical site map with publication priorities. Clustering is the foundation of building topical authority — without it you create content 'by eye', with it you have a data-based map.