What clustering does

Clustering groups points such that:

• points within a cluster are similar
• points across clusters are less similar

  flowchart LR
  X[Data points] --> C[Clustering algorithm] --> G[Cluster labels (groups)]

Similarity and distance

Most clustering methods rely on a notion of similarity, often distance.

Common distances:

• Euclidean (geometry)
• Manhattan
• cosine distance (common for text/embeddings)

Important: scaling impacts clustering

If your features are on different scales, distance-based clustering can fail.

Use scaling (StandardScaler/MinMaxScaler) when appropriate.

What makes clustering hard

There is usually no “ground truth”.

You validate using:

• domain sense (do clusters mean something?)
• internal metrics (silhouette score)
• stability across runs

Mini-checkpoint

If you’re clustering customers:

• what features would you use?
• what would a “useful” cluster look like in business terms?

🧪 Try It Yourself

Exercise 1 – Train-Test Split

Exercise 2 – Fit a Linear Model

Exercise 3 – Evaluate with MSE