clustering-analyzer
Cluster data using K-Means, DBSCAN, hierarchical clustering. Use for customer segmentation, pattern discovery, or data grouping.
npx skills add majiayu000/claude-skill-registry --skill clustering-analyzer-dkyazzentwatwa-chatgpt-skills --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Clustering Analyzer Analyze and cluster data using multiple algorithms with visualization and evaluation. ## Features - **K-Means**: Partition-based clustering with elbow method - **DBSCAN**: Density-based clustering for arbitrary shapes - **Hierarchical**: Agglomerative clustering with dendrograms - **Evaluation**: Silhouette scores, cluster statistics - **Visualization**: 2D/3D plots, dendrograms, elbow curves - **Export**: Labeled data, cluster summaries ## Quick Start ```python from clustering_analyzer import ClusteringAnalyzer analyzer = ClusteringAnalyzer() analyzer.load_csv("customers.csv") # K-Means clustering result = analyzer.kmeans(n_clusters=3) print(f"Silhouette Score: {result['silhouette_score']:.3f}") # Visualize analyzer.plot_clusters("clusters.png") ``` ## CLI Usage ```bash # K-Means clustering python clustering_analyzer.py --input data.csv --method kmeans --clusters 3 # Find optimal clusters (elbow method) python clustering_analyzer.py --input data.csv --method kmeans --find-optimal # DBSCAN clustering python clustering_analyzer.py --input data.csv --method dbscan --eps 0.5 --min-samples 5 # Hierarchical clustering python clustering_analyzer.py --input data.csv
- Features
- Quick Start
- CLI Usage
- API Reference
- ClusteringAnalyzer Class
- Clustering Methods
- K-Means
- DBSCAN
- Hierarchical (Agglomerative)
- Finding Optimal Clusters
- Elbow Method
- Elbow Plot
- Cluster Statistics
- Visualization
K-Means clustering python clustering_analyzer.py --input data.csv --method kmeans --clusters 3 Find optimal clusters (elbow method) python clustering_analyzer.py --input data.csv --method kmeans --find-optimal DBSCAN clustering python clustering_analyzer.py --input data.csv --method dbscan --eps 0.5 --min-samples 5 Hierarchical clustering python clustering_analyzer.py --input data.csv --method hierarchical --clusters 4 Generate plots python clustering_analyzer.py --input data.csv --method kmeans --clusters 3 --plot clusters.png
What does the clustering-analyzer skill do?
Cluster data using K-Means, DBSCAN, hierarchical clustering. Use for customer segmentation, pattern discovery, or data grouping.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill clustering-analyzer-dkyazzentwatwa-chatgpt-skills --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.