home / skills / jeremylongshore / claude-code-plugins-plus-skills / running-clustering-algorithms
/plugins/ai-ml/clustering-algorithm-runner/skills/running-clustering-algorithms
This skill analyzes datasets by running clustering algorithms to identify data groups and generate results with visuals and metrics.
npx playbooks add skill jeremylongshore/claude-code-plugins-plus-skills --skill running-clustering-algorithmsReview the files below or copy the command above to add this skill to your agents.
---
name: running-clustering-algorithms
description: |
Analyze datasets by running clustering algorithms (K-means, DBSCAN, hierarchical) to identify data groups. Use when requesting "run clustering", "cluster analysis", or "group data points". Trigger with relevant phrases based on skill purpose.
allowed-tools: Read, Write, Edit, Grep, Glob, Bash(cmd:*)
version: 1.0.0
author: Jeremy Longshore <[email protected]>
license: MIT
---
# Clustering Algorithm Runner
This skill provides automated assistance for clustering algorithm runner tasks.
## Overview
This skill empowers Claude to perform clustering analysis on provided datasets. It allows for automated execution of various clustering algorithms, providing insights into data groupings and structures.
## How It Works
1. **Analyzing the Context**: Claude analyzes the user's request to determine the dataset, desired clustering algorithm (if specified), and any specific requirements.
2. **Generating Code**: Claude generates Python code using appropriate ML libraries (e.g., scikit-learn) to perform the clustering task, including data loading, preprocessing, algorithm execution, and result visualization.
3. **Executing Clustering**: The generated code is executed, and the clustering algorithm is applied to the dataset.
4. **Providing Results**: Claude presents the results, including cluster assignments, performance metrics (e.g., silhouette score, Davies-Bouldin index), and visualizations (e.g., scatter plots with cluster labels).
## When to Use This Skill
This skill activates when you need to:
- Identify distinct groups within a dataset.
- Perform a cluster analysis to understand data structure.
- Run K-means, DBSCAN, or hierarchical clustering on a given dataset.
## Examples
### Example 1: Customer Segmentation
User request: "Run clustering on this customer data to identify customer segments. The data is in customer_data.csv."
The skill will:
1. Load the customer_data.csv dataset.
2. Perform K-means clustering to identify distinct customer segments based on their attributes.
3. Provide a visualization of the customer segments and their characteristics.
### Example 2: Anomaly Detection
User request: "Perform DBSCAN clustering on this network traffic data to identify anomalies. The data is available at network_traffic.txt."
The skill will:
1. Load the network_traffic.txt dataset.
2. Perform DBSCAN clustering to identify outliers representing anomalous network traffic.
3. Report the identified anomalies and their characteristics.
## Best Practices
- **Data Preprocessing**: Always preprocess the data (e.g., scaling, normalization) before applying clustering algorithms to improve performance and accuracy.
- **Algorithm Selection**: Choose the appropriate clustering algorithm based on the data characteristics and the desired outcome. K-means is suitable for spherical clusters, while DBSCAN is better for non-spherical clusters and anomaly detection.
- **Parameter Tuning**: Tune the parameters of the clustering algorithm (e.g., number of clusters in K-means, epsilon and min_samples in DBSCAN) to optimize the results.
## Integration
This skill can be integrated with data loading skills to retrieve datasets from various sources. It can also be combined with visualization skills to generate insightful visualizations of the clustering results.
## Prerequisites
- Appropriate file access permissions
- Required dependencies installed
## Instructions
1. Invoke this skill when the trigger conditions are met
2. Provide necessary context and parameters
3. Review the generated output
4. Apply modifications as needed
## Output
The skill produces structured output relevant to the task.
## Error Handling
- Invalid input: Prompts for correction
- Missing dependencies: Lists required components
- Permission errors: Suggests remediation steps
## Resources
- Project documentation
- Related skills and commands
This skill runs clustering algorithms (K-means, DBSCAN, hierarchical) to identify groups and patterns in tabular datasets. It automates data loading, preprocessing, algorithm selection, execution, and result reporting. The output includes cluster assignments, quality metrics, and optional visualizations to help interpret group structure.
The skill inspects the request to locate the dataset, chosen algorithm, and any parameter hints. It generates and runs Python code (commonly using scikit-learn and standard preprocessing tools) to clean, scale, and transform data as needed. The selected clustering method is executed, metrics like silhouette score or Davies–Bouldin index are calculated, and cluster labels plus plots are returned for review. Errors, missing dependencies, or permissions issues are reported with suggested fixes.
What input formats are supported?
CSV and common tabular formats are supported; provide a path or attach a dataset and the skill will load and infer types.
How do I choose between K-means, DBSCAN, and hierarchical?
Use K-means for spherical, similarly sized clusters; DBSCAN for irregular shapes and outlier detection; hierarchical when you need dendrograms or variable granularity.
What if I get poor clustering results?
Check preprocessing (scaling, missing values), try different algorithms, tune parameters, reduce noisy features, or try dimensionality reduction before clustering.