# Running Clustering Algorithms

> Segment data with clustering algorithms such as K-means, DBSCAN, or hierarchical clustering. Use for unsupervised grouping and cluster diagnostics, not supervised classification or publication-figure ownership.

- Skill: `majiayu000/running-clustering-algorithms-2` (Agent Skill, multi-file: 2 files)
- Install (CLI): `npx skillmds add majiayu000/running-clustering-algorithms-2`
- Raw SKILL.md: https://api.skillmd.com/api/skills/majiayu000/running-clustering-algorithms-2/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Coding & Dev Tools
- License: MIT
- Author: majiayu000 (https://skillmd.com/u/majiayu000)
- Updated: 2026-09-09
- Page: https://skillmd.com/skills/majiayu000/running-clustering-algorithms-2

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# Clustering Algorithm Runner

Use this skill when the main question is how to group unlabeled data points.

## Overview

This skill covers algorithm choice, preprocessing implications, cluster validation, and interpretation for unsupervised segmentation problems.

## When to Use This Skill

- Customer segmentation, cohort discovery, or grouping unlabeled records
- Choosing between centroid, density, or hierarchical clustering
- Reviewing silhouette score, Davies-Bouldin, or cluster stability

## Not For / Boundaries

- Supervised prediction with labels: use `training-machine-learning-models`
- Pure anomaly review without clustering as the central method: use `anomaly-detector`
- Final narrative report packaging: use `scientific-reporting`

## Typical Outputs

- Algorithm recommendation with parameter guidance
- Cluster-assignment workflow
- Validation and interpretation notes for cluster quality

## Related Skills

- `creating-data-visualizations` for exploratory plots of cluster structure
- `anomaly-detector` when outliers become the next question