Clustering Result #33 — Analysis Report
Generated 2026-10-16 08:49:20 · 1 run · CAT-G Data Analyzer
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- 1. kmeans (#33) completed
1. Kmeans
2026-09-21 02:17:13 · clustering
Clustering split 75 samples into 3 clusters (mean silhouette 0.479).
| method | kmeans |
|---|---|
| k | 3 |
| silhouette | 0.479172 |
| n_rows | 75 |
| n_dropped | 0 |
| derived_dataset_id | 713 |
sizes
| name | value |
|---|---|
| 0 | 25 |
| 1 | 25 |
| 2 | 25 |
inertia sweep
| name | value |
|---|---|
| 2 | 285.420962 |
| 3 | 164.583327 |
| 4 | 138.258257 |
| 5 | 122.455501 |
| 6 | 104.3279 |
| 7 | 92.095338 |
| 8 | 81.483783 |
| 9 | 73.163786 |
| 10 | 65.989041 |
cluster scatter
silhouette
elbow
Method & Provenance
- Method
- Clustering — K-Means
- Engine
- Python · scikit-learn 1.8.0
- Input dataset
- gallery — example_clustering.csv
- Started
- 2026-09-21 02:17:13.556421
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/952c914477ed424c80fc8abe48b2dd16
| Parameter | Value |
|---|---|
cluster_method
| kmeans |
feature_cols
| f1, f2, f3, f4, f5, f6 |
n_clusters
| 3 |
Command history
# python from sklearn.cluster import KMeans # python labels = KMeans(n_clusters=3, n_init=10, random_state=42).fit_predict(X) # python silhouette = sklearn.metrics.silhouette_score(X, labels)