Build Keyword Clusters from Raw Text Data
Clustering fails when source data is noisy. Duplicate terms, mixed casing, and irrelevant modifiers can produce confusing groups that look comprehensive but do not map cleanly to user intent.
Before clustering, deduplicate lines, normalize case, and remove obvious noise words. Then filter terms by includes and excludes to isolate topics such as transactional intent, feature comparisons, or beginner queries.
After cleanup, group terms by semantic closeness and verify each cluster with a clear page purpose. One cluster should serve one primary intent to avoid cannibalization and weak messaging.
Clean clustering creates better editorial plans and stronger internal linking architecture. It turns raw keyword lists into actionable publishing strategy.