Kmeans run #33 · clustering
3
n clusters
kmeans
method
3
k
0.4792
silhouette
completed
status
2026-09-21 02:17
created
Figures
Sizes 3 rows
| name | value |
|---|---|
| 0 | 25 |
| 1 | 25 |
| 2 | 25 |
Inertia Sweep 9 rows
| name | value |
|---|---|
| 2 | 285.4209620844338 |
| 3 | 164.58332698339277 |
| 4 | 138.25825742685765 |
| 5 | 122.45550139941665 |
| 6 | 104.3279001799331 |
| 7 | 92.09533844013842 |
| 8 | 81.483782546419 |
| 9 | 73.16378637539616 |
| 10 | 65.98904065200387 |
Method & Provenance
- Method
- Clustering — K-Means
- Method key
kmeans(result type:clustering, result #33)- Engine
- Python · scikit-learn v1.8.0
- Dataset(s)
- #712 gallery — example_clustering.csv
- Status
- completed
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/952c914477ed424c80fc8abe48b2dd16 Bookmark or share this URL — it does not depend on your session.
- Created
- 2026-09-21 02:17:13.556421
Parameters used
| Parameter | Value |
|---|---|
cluster_method
| kmeans |
feature_cols
| f1, f2, f3, f4, f5, f6 |
n_clusters
| 3 |
Execution trace
Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made.
from sklearn.cluster import KMeanslabels = KMeans(n_clusters=3, n_init=10, random_state=42).fit_predict(X)silhouette = sklearn.metrics.silhouette_score(X, labels)