🤖 Machine Learning Clustering Algorithms

A curated list of 20 clustering algorithms implemented in or accessible via Scikit-learn 🧠
These algorithms are widely used for unsupervised learning, pattern discovery, and data segmentation.

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📊 Clustering Algorithms Overview

🔢 Serial No.	🧩 Algorithm Name	📦 Scikit-learn Import Path
1	K-Means	from sklearn.cluster import KMeans
2	MiniBatch K-Means	from sklearn.cluster import MiniBatchKMeans
3	Agglomerative Clustering	from sklearn.cluster import AgglomerativeClustering
4	DBSCAN	from sklearn.cluster import DBSCAN
5	OPTICS	from sklearn.cluster import OPTICS
6	Mean Shift	from sklearn.cluster import MeanShift
7	Spectral Clustering	from sklearn.cluster import SpectralClustering
8	Birch	from sklearn.cluster import Birch
9	Affinity Propagation	from sklearn.cluster import AffinityPropagation
10	Gaussian Mixture Model (GMM)	from sklearn.mixture import GaussianMixture
11	Bayesian Gaussian Mixture	from sklearn.mixture import BayesianGaussianMixture
12	Feature Agglomeration	from sklearn.cluster import FeatureAgglomeration
13	Bisecting K-Means	from sklearn.cluster import BisectingKMeans
14	K-Medoids	from sklearn_extra.cluster import KMedoids (scikit-learn-extra)
15	Fuzzy C-Means	from fcmeans import FCM (external library)
16	Self-Organizing Maps (SOM)	from minisom import MiniSom (external library)
17	HDBSCAN	from hdbscan import HDBSCAN (external library)
18	Spectral Biclustering	from sklearn.cluster import SpectralBiclustering
19	Spectral Coclustering	from sklearn.cluster import SpectralCoclustering
20	Ward Hierarchical Clustering	from sklearn.cluster import AgglomerativeClustering (with linkage='ward')

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🚀 Usage Example

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs

# Sample data
X, _ = make_blobs(n_samples=300, centers=3, random_state=42)

# Initialize and fit model
model = KMeans(n_clusters=3, random_state=42)
model.fit(X)

print(model.labels_)