Clustering Result #34 — Analysis Report
Generated 2026-10-15 05:25:53 · 1 run · CAT-G Data Analyzer
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- 1. agglomerative (#34) completed
1. Agglomerative
2026-09-21 02:17:18 · clustering
Clustering split 75 samples into 3 clusters (mean silhouette 0.479).
| method | agglomerative |
|---|---|
| k | 3 |
| linkage | ward |
| silhouette | 0.479172 |
| n_rows | 75 |
| n_dropped | 0 |
| derived_dataset_id | 714 |
sizes
| name | value |
|---|---|
| 0 | 25 |
| 1 | 25 |
| 2 | 25 |
cluster scatter
silhouette
dendrogram
Method & Provenance
- Method
- Clustering — Agglomerative clustering
- Engine
- Python · scikit-learn 1.8.0
- Input dataset
- gallery — example_clustering.csv
- Started
- 2026-09-21 02:17:18.284433
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/1a3dd8c3013a4c3bb4c0baa519c650e2
| Parameter | Value |
|---|---|
cluster_method
| agglomerative |
feature_cols
| f1, f2, f3, f4, f5, f6 |
n_clusters
| 3 |
linkage
| ward |
Command history
# python from sklearn.cluster import AgglomerativeClustering # python labels = AgglomerativeClustering(n_clusters=3, linkage='ward').fit_predict(X) # python silhouette = sklearn.metrics.silhouette_score(X, labels)