Clustering with K-Means

Group similar things together.

What is K-Means?

Automatically groups similar data points.

No labels needed! (Unsupervised learning)

Real Example

Group customers in San Francisco by shopping behavior:

Cluster 1: Young, low spending
Cluster 2: Middle-age, medium spending
Cluster 3: Older, high spending

How It Works

1. Choose K (number of groups)
2. Place K random center points
3. Assign each point to nearest center
4. Move centers to group average
5. Repeat until stable

Python Code

from sklearn.cluster import KMeans
import numpy as np

# Customer data: [age, annual_purchases]
X = np.array([
    [25, 10], [22, 8], [50, 50], 
    [48, 55], [70, 80], [72, 85]
])

# Find 3 clusters
model = KMeans(n_clusters=3)
model.fit(X)

# Get cluster assignments
labels = model.labels_
print(labels)  # [0, 0, 1, 1, 2, 2]

# Predict cluster for new customer
new_customer = [[30, 15]]
cluster = model.predict(new_customer)
print(f"Customer belongs to cluster {cluster[0]}")

Choosing K

Use "Elbow Method":

• Try different K values
• Plot total distance
• Choose "elbow" point

Applications

• Customer segmentation
• Image compression
• Document clustering
• Anomaly detection

Advantages

• Simple and fast
• Scales to large data
• Easy to implement

Disadvantages

• Must choose K manually
• Sensitive to initial centers
• Assumes spherical clusters

Remember

• Start with 3-5 clusters
• Scale features first
• Try multiple runs