# Ml Unsupervised Clustering

> Discovering patterns in unlabeled data using clustering and dimensionality reduction.

- Skill: `jcorpac/ml-unsupervised-clustering` (Agent Skill)
- Install (CLI): `npx skillmds@latest add jcorpac/ml-unsupervised-clustering`
- Raw SKILL.md: https://api.skillmd.com/api/skills/jcorpac/ml-unsupervised-clustering/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- Author: jcorpac (https://skillmd.com/u/jcorpac)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/jcorpac/ml-unsupervised-clustering

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# ML Unsupervised Clustering

Unsupervised learning finds hidden structures in data without explicit labels.

## Core Techniques
- **K-Means**: Simple and effective for spherical clusters. Use the "Elbow Method" to find the optimal `k`.
- **DBSCAN**: Density-based clustering that can handle irregular shapes and noise.
- **PCA (Principal Component Analysis)**: Reducing feature space while retaining variance. Essential for visualization and avoiding the "curse of dimensionality."

## Evaluation Metrics
Since there are no labels, we use:
- **Silhouette Score**: Measuring cluster separation and cohesion.
- **Inertia**: Sum of squared distances to cluster centers.

## Best Practices
- **Feature Scaling**: Essential for distance-based algorithms like K-Means.
- **Interpretablity**: Plot your results (e.g., using t-SNE or UMAP for high-dimensional data).