Clustering Result #35 — Analysis Report
Generated 2026-10-14 21:31:56 · 1 run · CAT-G Data Analyzer
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- 1. kmeans (#35) completed
1. Kmeans
2026-09-21 02:18:13 · clustering
Clustering split 150 samples into 3 clusters (mean silhouette 0.349).
| method | kmeans |
|---|---|
| k | 3 |
| silhouette | 0.348815 |
| n_rows | 150 |
| n_dropped | 0 |
| derived_dataset_id | 723 |
sizes
| name | value |
|---|---|
| 0 | 120 |
| 1 | 15 |
| 2 | 15 |
inertia sweep
| name | value |
|---|---|
| 2 | 7622.387217 |
| 3 | 6661.63414 |
| 4 | 6197.62538 |
| 5 | 5589.571419 |
| 6 | 5319.345703 |
| 7 | 4789.599972 |
| 8 | 4433.637965 |
| 9 | 4054.299986 |
| 10 | 3834.379517 |
cluster scatter
silhouette
elbow
Method & Provenance
- Method
- Clustering — K-Means
- Engine
- Python · scikit-learn 1.8.0
- Input dataset
- gallery — cat_g_proteomics_omics_only.csv
- Started
- 2026-09-21 02:18:13.933986
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/353bac72a0db47cbbc54087c03123afd
| Parameter | Value |
|---|---|
cluster_method
| kmeans |
feature_cols
| Psat1g153760.1;Psat2g159360.1, Psat4g000800.1, Psat7g234480.1, Psat6g170320.2, P13918;Psat0s5g0160.1;Psat5g199920.1, Psat0s3063g0160.1, Psat0s1401g0040.1, Psat1g046920.1;Psat3g040960.1;Psat6g072360.3, Psat4g103520.1, A0A0K0KZR1, Psat0s1587g0080.1, D3VNE1;D3VNE2, Psat3g204960.1, Psat4g152760.1, Psat3g066960.1;Psat4g123280.1, Psat1g060840.1, Psat3g040960.1;Q04593, Psat4g095120.1, P82333, Psat0s2970g0080.1, Psat2g102880.1, Psat5g016560.1, Psat0s2008g0040.2;Psat1g060840.1, Psat1g141040.1;Psat1g141080.3, Psat2g028040.1, Psat2g036360.1;Psat2g036520.1, Psat5g111120.1, Psat1g136200.1, Psat1g046920.1;Psat3g040960.1, P19251, Q9SSY8, Psat1g107440.1;Psat2g149200.1;Psat4g184760.1;Psat4g185080.1, Psat6g235800.1;Psat6g235800.2, Psat2g036360.1;Psat2g036480.1;Psat2g036520.1, Q7M228, Q8LP17, Psat1g046920.1;Psat3g040960.1;Q04593, Psat1g107440.1;Psat4g184440.1, Psat1g046920.1;Psat3g040960.1;Psat6g182520.1;Q04593, Psat6g178400.1, A0A432N9M1, Psat6g054880.1, Psat4g213880.1, Psat2g036480.1, Psat7g102240.1, Psat0s1664g0080.1, Psat1g130000.1, A0A432NQN8, Psat3g094480.1, Psat1g046920.1;Psat3g040960.1;Psat6g072360.3;Psat6g182520.1, Psat3g197600.1, Q8VX19, Psat4g184440.1;Psat4g184760.1, Psat2g169200.1, Psat1g194520.1;Psat7g068160.1, Psat3g010120.1, Psat4g194120.1, Psat2g183320.1;Psat3g179120.1, Psat2g028440.1, P51082 |
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)