Community Contributed Anomaly Detection Medoids (Unverified)
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This template helps identify representative data points (medoids) for anomaly detection within specific crops from a community-contributed dataset.
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About This Workflow
Overview
This workflow utilizes community-contributed data and anomaly detection techniques to find 'medoids' for different crop types. Medoids are representative points within a cluster that can be used to define normal behavior, making them valuable for identifying outliers or anomalies.
This template is community-contributed and unverified, meaning it has not undergone formal n8n review but can be a starting point for your automation needs.
Key Features
- Identifies medoids within clusters of crop data.
- Leverages community-contributed data for analysis.
- Integrates with Qdrant for vector similarity searches.
- Calculates distance matrices and identifies the most central point in a cluster.
- Sets metadata for identified medoids and cluster thresholds.
How To Use
- Import the workflow: Import this JSON into your n8n instance.
- Configure Credentials: Set up your Qdrant API credentials.
- Set Environment Variables: Ensure
BASE_URLis configured for your Qdrant instance. - Adjust Node Parameters: Modify the
jsonBodyparameters in the HTTP Request nodes to match your specific collection names, vector configurations (e.g., 'voyage'), and desiredcrop_namefilters. - Test and Run: Trigger the workflow manually or schedule it for automated execution.
Apps Used
Workflow JSON
{
"id": "0d56221f-a46a-4483-9322-183ae192b5a6",
"name": "Community Contributed Anomaly Detection Medoids (Unverified)",
"nodes": 0,
"category": "Community Contributed",
"status": "active",
"version": "1.0.0"
}Note: This is a sample preview. The full workflow JSON contains node configurations, credentials placeholders, and execution logic.
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ID: 0d56221f-a46a...
About the Author
SaaS_Connector
Integration Guru
Connecting CRM, Notion, and Slack to automate your life.
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