Data Science — Clustering

K-means

from sklearn.cluster import KMeans
from sklearn.datasets import make_blobs
import matplotlib.pyplot as plt

X, y = make_blobs(n_samples=300, centers=4, random_state=42)

kmeans = KMeans(n_clusters=4, random_state=42)
labels = kmeans.fit_predict(X)

plt.scatter(X[:, 0], X[:, 1], c=labels)
plt.scatter(kmeans.cluster_centers_[:, 0], kmeans.cluster_centers_[:, 1], c='red', marker='x')
plt.show()

Elbow method

inertias = []
for k in range(1, 10):
    kmeans = KMeans(n_clusters=k, random_state=42)
    kmeans.fit(X)
    inertias.append(kmeans.inertia_)

plt.plot(range(1, 10), inertias)
plt.xlabel('K')
plt.ylabel('Inertia')
plt.show()

DBSCAN

from sklearn.cluster import DBSCAN

dbscan = DBSCAN(eps=0.5, min_samples=5)
labels = dbscan.fit_predict(X)

Hierarchical clustering

from sklearn.cluster import AgglomerativeClustering

hierarchical = AgglomerativeClustering(n_clusters=4)
labels = hierarchical.fit_predict(X)

Evaluation

from sklearn.metrics import silhouette_score

score = silhouette_score(X, labels)
print(f"Silhouette score: {score:.4f}")

Mini Practice

1. Apply K-means
2. Find optimal K
3. Try DBSCAN
4. Evaluate clusters

Up Next

Continue with Dimensionality Reduction - Feature reduction.