Clustering Algorithm Runner

소개

This skill empowers Claude to perform end-to-end clustering analysis using industry-standard algorithms such as K-means, DBSCAN, and hierarchical clustering. It automates the entire data science workflow—from initial data preprocessing and feature scaling to the execution of models using scikit-learn and the generation of performance metrics like Silhouette scores. By providing automated visualizations and parameter tuning, this skill is essential for developers and data scientists looking to perform customer segmentation, anomaly detection, or exploratory data analysis directly within their coding environment.

주요 기능

• Support for K-means, DBSCAN, and hierarchical clustering algorithms
• Automated data preprocessing including scaling and normalization
• Robust error handling and data validation for various file formats
• Integrated visualization capabilities for scatter plots and cluster labeling
• Generation of performance metrics such as Silhouette and Davies-Bouldin indices
• 3 GitHub stars

사용 사례

• Segmenting customer data into distinct behavioral groups for marketing
• Discovering hidden structures and relationships in unlabeled datasets
• Detecting anomalies and outliers in network traffic or server logs