Finding groups when you have NO labels (customer segments, anomalies, topics). Nine algorithms, each returning labels plus a quality score.
Pick a model, press Run, and it trains and cross-validates on the spot, in your browser.
Results will appear here.
call as breezeml.clustering.<name>(df).
| Model | Call | Good for |
|---|---|---|
| K-Means | kmeans | Fast, round clusters when you know how many (k). |
| Agglomerative | agglomerative | Builds a hierarchy of nested groups. |
| DBSCAN | dbscan | Finds dense blobs of any shape; flags noise. |
| HDBSCAN | hdbscan | DBSCAN that picks the density for you. |
| Gaussian Mixture | gaussian_mixture | Soft, elliptical clusters with probabilities. |
| BIRCH | birch | Memory-friendly clustering for large data. |
| Spectral | spectral | Graph-based; great on non-convex shapes. |
| Mean Shift | meanshift | Finds clusters without setting k. |
| OPTICS | optics | Density clustering across varying scales. |
clustering.kmeans(df, n_clusters=3) -> dictReturns the cluster labels and a silhouette score (how well-separated the groups are).
In plain English: Sorts rows into k groups by similarity and tells you how cleanly they separated.
import breezeml
df = breezeml.datasets.iris().drop(columns=["species"])
res = breezeml.clustering.kmeans(df, n_clusters=3)
print("silhouette:", round(res["silhouette"], 3))unsupervised groupingRun hereClustering gets NO answer column. It groups points purely by how close they are. Press Run to watch it rediscover the flower groups using only two measurements, no labels at all.
In plain English: No answer key at all. It groups points just by how close together they sit, and often rediscovers the real groups on its own.
Press Run to execute this snippet.