Clustering Algorithms

Introduction

Clustering groups similar data points without predefined labels.

K-Means

from sklearn.cluster import KMeans

kmeans = KMeans(n_clusters=3, random_state=42)
labels = kmeans.fit_predict(X)

# Elbow method
inertias = []
for k in range(1, 11):
    kmeans = KMeans(n_clusters=k)
    kmeans.fit(X)
    inertias.append(kmeans.inertia_)

Hierarchical Clustering

from sklearn.cluster import AgglomerativeClustering

agg = AgglomerativeClustering(n_clusters=3, linkage="ward")
labels = agg.fit_predict(X)

# Dendrogram
from scipy.cluster.hierarchy import dendrogram, linkage
Z = linkage(X, method="ward")
dendrogram(Z)

DBSCAN

from sklearn.cluster import DBSCAN

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

Practice Problems

1. Find optimal k with elbow method
2. Compare clustering algorithms
3. Evaluate with silhouette score
4. Visualize clusters
5. Use hierarchical clustering