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 1w 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 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.
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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