Clustering Result #32 — Analysis Report

Generated 2026-10-15 10:20:01 · 1 run · CAT-G Data Analyzer

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Contents

1. 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)