Result Detail – Clustering Help

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Result Information
Result ID
34
Type
Clustering
Status
completed
Dataset ID
712
Created
2026-09-21 02:17:18.284433
Clustering Result – Agglomerative Help
Method
Agglomerative
Clusters (k)
3
Silhouette score
0.4792 (higher = better separated)
Rows clustered
75
Feature columns
f1, f2, f3, f4, f5, f6
Cluster sizes Help
Cluster 0: n=25 Cluster 1: n=25 Cluster 2: n=25
Per-cluster feature means Help
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
Cluster scatter
Silhouette plot Download PNG
Silhouette plot
Hierarchical clustering dendrogram Download PNG
Hierarchical clustering dendrogram
Pathway Analysis (same dataset) Help
Run Pathway Analysis

Method & Provenance

Method
Clustering — Agglomerative clustering
Method key
agglomerative (result type: clustering, result #34)
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/1a3dd8c3013a4c3bb4c0baa519c650e2 Bookmark or share this URL — it does not depend on your session.
Created
2026-09-21 02:17:18.284433

Parameters used

ParameterValue
cluster_method agglomerative
feature_cols f1, f2, f3, f4, f5, f6
n_clusters 3
linkage ward

Execution trace

Reconstructed from the parameters stored with this result — represents the Python/R calls the analysis service made. 

from sklearn.cluster import AgglomerativeClustering
labels = AgglomerativeClustering(n_clusters=3, linkage='ward').fit_predict(X)
silhouette = sklearn.metrics.silhouette_score(X, labels)