keyword-clustering-minhash
🥈SilverThis Python script clusters keywords based on the similarity of their associated URLs using MinHash and MinHashLSH. The clustering process helps identify keywords that return similar search engine result pages (SERPs), which can be useful for SEO and content optimization strategies.
730Updated 2mo ago
Intermediate30min to implementmarketing
Saves ~120 min per use
Quick InstallView Source
git clone https://github.com/dartseoengineer/keyword-clustering-minhash.gitWorks with:
Claude
Overview
About This Skill
This Python script identifies keyword clusters by analyzing the similarity of their associated search engine result pages (SERPs). It uses MinHash to create similarity sketches of URLs and Locality Sensitive Hashing (LSH) to group keywords that return comparable results. The tool helps SEO professionals discover keywords with overlapping search results, enabling more strategic content optimization and competitive analysis. By processing CSV input files with configurable thresholds, it outputs clustered keywords that share similar SERP landscapes, streamlining keyword research workflows.
How to Use
Install dependencies with `pip install pandas tqdm datasketch`. Run the script via command line: `python minhash-cluster-cli.py input.csv output.csv` with optional parameters for file separator, column names, and similarity threshold (default 0.6). The script outputs a CSV with Group IDs and clustered keywords.
Use Cases
Identify keyword opportunities with overlapping SERPs for content consolidation
Discover competitor keywords ranking in similar search results
Prioritize content optimization by grouping related keyword clusters
Analyze keyword difficulty by clustering based on search result similarity
Tags
Setup & Installation
Quick Install
No install command available. Check the GitHub repository for manual installation instructions.
Alternative Install (Git Clone)
git clone https://github.com/dartseoengineer/keyword-clustering-minhashRequirements
- Claude Code or compatible AI agent
- Works with: Claude
Quick Start Guide
1
Install the Skill
Copy the install command above and run it in your terminal.
2
Open Your AI Agent
Launch Claude Code, Cursor, or your preferred AI coding agent.
3
Try It Out
Use the prompt template or examples below to test the skill.
4
Customize
Adapt the skill to your specific use case and workflow.
Usage Examples
Prompt Template
I need to cluster these keywords based on their associated URLs using MinHash and MinHashLSH. Here's the [DATA] in CSV format: [PASTE DATA]. Can you provide the Python code to perform this clustering and explain the results?
Example Output
# Keyword Clustering Results ## Cluster 1: E-commerce Platforms - **Keywords**: online shopping, best deals, discount codes - **Top URLs**: amazon.com, ebay.com, walmart.com - **Similarity Score**: 0.85 ## Cluster 2: Digital Marketing Tools - **Keywords**: SEO tools, marketing automation, email campaigns - **Top URLs**: hubspot.com, mailchimp.com, moz.com - **Similarity Score**: 0.78 ## Cluster 3: Health and Wellness - **Keywords**: fitness tips, healthy recipes, mental health - **Top URLs**: healthline.com, webmd.com, verywellfit.com - **Similarity Score**: 0.82 ### Analysis The clustering algorithm identified three distinct groups of keywords that share similar SERPs. This suggests that search engines are grouping these keywords together in their ranking algorithms. Marketers can use this information to optimize content for these clusters and improve their SEO strategies.
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