7 unique algorithms: Jaccard, BM25, PMI-semantic, Ward-hierarchy, DBSCAN, Levenshtein
💡 Підтримується будь-який формат: з частотностями або без, з будь-яким розділювачем (таб, крапка з комою, пробіл тощо)
Compare all and select best
When unsure which to choose
Less = more keywords per cluster
More = fewer final clusters
Fundamental SEO technique for creating effective site structure
Keyword clustering is the process of automatically grouping semantically similar search queries into logical groups (clusters). Each cluster represents one topic corresponding to one page on your site. This allows you to create content that maximally matches user search intent and Google algorithm requirements.
We use 7 different machine learning algorithms: TF-IDF, Jaccard similarity, N-grams, Levenshtein distance, semantic co-occurrence analysis, and hierarchical clustering. The system automatically compares results from all methods and recommends the best one for your data.
8 advanced NLP techniques for the most accurate grouping
Term frequency-inverse document frequency weighting. Rare words get higher weight for more accurate clustering.
Bigrams, trigrams and Jaccard coefficient for comparing phrase similarity.
Edit distance for detecting typos and spelling variations.
Agglomerative algorithm with average linkage for optimal cluster merging.
Word co-occurrence matrix for detecting semantic relationships between terms.
TF-IDF vector cosine similarity for comparing with cluster centroids.
50+ stemming rules for Ukrainian and English with morphology support.
Automatic removal of 100+ function words for both languages for cleaner analysis.
Four simple steps to perfect site structure
Copy your keyword list from Excel, Google Sheets or any other source. Each keyword should be on a new line.
Basic method is fast and effective. Advanced creates more precise subgroups using Levenshtein algorithm.
The system will automatically group keywords by semantic similarity and show relevance level for each word.
Download ready clusters in CSV or JSON format for further work in Excel, Google Sheets or your CMS.
Choose method based on data size and needed precision
Fast grouping with combined similarity metric
Cluster centroids with TF-IDF weighting
Word co-occurrence analysis in context
Bottom-up agglomerative clustering
Why thousands of SEO specialists use our tool daily
Properly structured content ranks higher in Google. Clustering helps create pages with maximum relevance.
Automatic processing of thousands of keywords in seconds instead of hours of manual work. More time for strategy, less for routine.
Each page focuses on one topic with all related keywords. Google better understands your content.
Clusters naturally form page hierarchy: categories, subcategories, articles. Improves UX and indexation.
One page = one cluster = one search intent. No conflicting pages fighting for the same keywords.
Process up to 50,000 keywords per request. Perfect for large projects and agencies.
Tool for professionals and businesses of any scale
Building semantic core, planning site structure, optimizing content strategy
Example: Clustering 10,000 keywords for an electronics online store
Fast processing of large keyword volumes for client projects
Example: Client site audit and restructuring in 1 day
Planning articles with full topic coverage and all LSI keywords
Example: Creating content plan for 100 blog articles
Optimizing product categories and creating SEO descriptions
Example: Structuring catalog of 5,000 products
Finding article topics and avoiding content duplication
Example: Niche analysis and publication planning for the year
Understanding what customers search for and optimizing site for their needs
Example: Competitor analysis and finding untapped niches
Based on reviews from 1,247 users
🔒 Your data never leaves your browser. All processing happens locally.
Everything you need to know about keyword clustering for SEO
Keyword clustering is the process of automatically grouping semantically similar search queries. One cluster = one relevant page on your site. This is the foundation for building site structure and content strategy.
Proper clustering can increase organic traffic by 30-50% through better content relevance.
Clustering helps: 1) Avoid keyword cannibalization when multiple pages compete for the same queries. 2) Create logical site structure. 3) Optimize internal linking. 4) Increase relevance of each page.
Optimally 5-20 keywords per cluster. Less than 5 — consider merging with another cluster. More than 20 — consider splitting into subclusters or creating a series of pages.
If a cluster contains more than 50 queries — this signals creating a hub page with subpages.
Auto — if unsure, system compares all methods. By Intent — for e-commerce and landing pages. By Rare Words — for niche sites. By Structure — for information portals. By Tail — for local SEO.
Similarity (0-100%) shows how well a keyword matches the cluster topic. >80% — perfect match, content foundation. 60-80% — good, include it. 40-60% — questionable, check manually. <40% — probably belongs to another cluster.
Filter results by similarity to exclude irrelevant queries.
Silhouette Score (-1 to 1) — clustering quality metric. >0.5 — excellent, clear groups. 0.25-0.5 — good, some overlap. 0-0.25 — satisfactory, consider another method. <0 — poor, clusters are mixed.
Available formats: CSV — for Excel, Google Sheets, Power BI. Contains cluster, keyword, similarity. JSON — for developers and CMS integrations. Structured format with metadata.
1) Export to CSV. 2) Each cluster = one article/page. 3) Cluster name — main keyword for Title. 4) Other cluster words — LSI for text. 5) Similarity determines inclusion priority.
Sort clusters by size — larger clusters usually have higher search potential.
No, absolutely not. All processing happens locally in your browser using JavaScript. Your data is not sent to server, not stored, and not analyzed by third parties.
Yes. Since processing is completely local, your business data including competitive analysis and semantic core remain confidential. No data leaves your browser.
Up to 50,000 keywords per request. Speed depends on your device. Approximately: 1,000 words — 1-2 sec, 10,000 — 10-20 sec, 50,000 — 1-3 min.
For large lists we recommend using By Intent method — it's the fastest.
Possible reasons: 1) Too many keywords (>10,000). 2) Using resource-intensive method (Semantic, Hierarchical). 3) Weak device or many open tabs. Try closing other programs.
Any format is supported: TAB-separated (keyword TAB 1000), semicolon (keyword;1000), in brackets (keyword (1000)), or space-separated (keyword 5000). You can also drag-and-drop a CSV/TXT file. The system automatically detects the format.
Numbers below 100 (e.g., 'iphone 15', 'top 10') are NOT parsed as frequency — they remain part of the keyword.
The system automatically sums up the search volume of all keywords in a cluster. This shows the total search potential of each group and helps prioritize content for the highest-volume clusters.
Sort clusters by total frequency to find the most valuable SEO topics.
Any exports from popular SEO tools are supported: Serpstat, Ahrefs, SEMrush, Google Keyword Planner, as well as CSV, TSV, TXT files. The system automatically finds the required columns (keyword, volume, etc.) regardless of their position in the file. Just drag-and-drop the file — everything else is handled automatically.
No need to edit files — the system smartly detects columns even if the file has lots of extra data.
Simply export keywords from any of these services in CSV or Excel format, then drag-and-drop the file into the input field. The system will automatically find columns with keywords and search volume, regardless of file structure and column names.
All report types are supported: positions, competitors, keywords, domain analysis, etc.