Keyword Clustering

Keyword clustering is the process of organizing keywords into groups based on semantic similarity and search intent overlap. Instead of treating each keyword as a standalone target, clustering identifies which terms share enough meaning that a single, comprehensive page can rank for all of them. Modern clustering uses vector embeddings to measure similarity between terms, then applies a distance threshold to form groups. The result is a topic map that shows which clusters have high combined search volume, which are already covered by existing content, and where gaps exist. Effective clustering reduces content cannibalization (where multiple pages compete for the same queries) and helps teams build topical authority by covering clusters comprehensively rather than publishing thin pages for individual keywords.