Clustering Result #32 — Analysis Report
Contents
- kmeans (#32) completed
1. Kmeans
2026-09-21 02:12:45 · clustering
Clustering split 59 samples into 3 clusters (mean silhouette 0.152).
| method | kmeans |
|---|---|
| k | 3 |
| silhouette | 0.152473 |
| n_rows | 59 |
| n_dropped | 341 |
| derived_dataset_id | 693 |
sizes
| name | value |
|---|---|
| 0 | 9 |
| 1 | 35 |
| 2 | 15 |
inertia sweep
| name | value |
|---|---|
| 2 | 2093.887449 |
| 3 | 1847.72523 |
| 4 | 1699.257911 |
| 5 | 1613.733829 |
| 6 | 1523.590748 |
| 7 | 1408.770415 |
| 8 | 1339.767584 |
| 9 | 1255.414151 |
| 10 | 1173.445572 |
cluster scatter
silhouette
elbow
Method & Provenance
- Method
- Clustering — K-Means
- Engine
- Python · scikit-learn 1.8.0
- Input dataset
- gallery — cat_g_field_data_plots.csv
- Started
- 2026-09-21 02:12:45.307195
- Permanent link
- https://cat-g-omxpert.wishartlab.com/results/by-token/a038a07d3c7e4e02ab3ee272769ecd2c
| Parameter | Value |
|---|---|
| cluster_method | kmeans |
| feature_cols | PLOT, LAT, LONG, ENV_SOILNH4, ENV_SOILNO3, ENV_SOILP, ENV_SOILOC, ENV_SOILTN, ENV_SOILTC, ENV_PH, ENV_SOILCLAY, ENV_SOILSILT, ENV_SOILSAND, ENV_SOILMOIS, RC_SHOOTMASS, RC_WOODMASS, RC_LITTERMASS, RC_LITTERWMASS, PC_SHOOTMASS, PC_WOODMASS, PC_LITTERMASS, PC_LITTERWMASS, PC_ROOTMASS, PC_HEIGHT, GH_COUNT, ENV_AIRTEMP, COVER_RICHNESS_JUN, COVER_TOTAL_JUN, COVER_SHANNON_JUN, COVER_RICHNESS_JUL, COVER_TOTAL_JUL, COVER_SHANNON_JUL, COVER_RICHNESS_AUG, COVER_TOTAL_AUG, COVER_SHANNON_AUG, PASTURE_TOTALAREA, SUBPASTURE_TOTALAREA, PASTURE_FORESTAREA, PASTURE_WETLANDAREA, SUBPASTURE_FORESTAREA, SUBPASTURE_WETLANDAREA |
| 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)