Result Detail – Clustering

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Result Information

Result ID: 33
Type: Clustering
Status: completed
Dataset ID: 712
Created: 2026-09-21 02:17:13.556421

Clustering Result – Kmeans

Method: Kmeans
Clusters (k): 3
Silhouette score: 0.4792 (higher = better separated)
Rows clustered: 75
Feature columns: f1, f2, f3, f4, f5, f6

Cluster sizes

Cluster 0: n=25 Cluster 1: n=25 Cluster 2: n=25

Per-cluster feature means

Cluster	f1	f2	f3	f4	f5	f6
0	6.134	6.045	5.815	-0.05513	-0.3117	0.1327
1	-0.03784	8.314	0.04768	6.028	0.02641	-0.1617
2	0.08804	0.121	-0.06625	-0.05106	-0.1555	-0.07231

Figures

Cluster scatter Download PNG

Silhouette plot Download PNG

Elbow plot Download PNG

Derived Dataset — Ready for Downstream Analysis

The output of this run was saved as a new dataset, gallery — example_clustering.csv (clustered), which you can select in any downstream analysis (machine learning, statistical tests, further preprocessing, or feature selection).

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Pathway Analysis (same dataset)

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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 KMeans

labels = KMeans(n_clusters=3, n_init=10, random_state=42).fit_predict(X)

silhouette = sklearn.metrics.silhouette_score(X, labels)