Clustering and Dimensionality Reduction

Why this module belongs in the course

Clustering always returns structure when asked. Validation must test whether that structure is stable, interpretable and useful, not merely colourful in a chart.

Before you begin

The concepts and project evidence from Module 5. You should also be able to create a Python virtual environment and keep private or employer data out of the exercise.

Four lessons, one connected result

1. Lesson 1K-Means and K-Medoids Clustering with Python85 min · Intermediate
2. Lesson 2Hierarchical Clustering, Linkage and Dendrograms85 min · Intermediate
3. Lesson 3DBSCAN and OPTICS Density-Based Clustering90 min · Intermediate
4. Lesson 4Project: Reduce Dimensions and Validate a Cluster Solution155 min · Intermediate

How to know you are ready to continue

Complete the checkpoint without copying the worked example. Keep the code, output and a short decision note. Your note should explain one choice, one failure you observed and one limitation a reviewer should know.

Primary references for this module

The lessons explain the ideas in original wording. Use these primary or official sources when a library interface, standard or research claim needs verification.

• scikit-learn k-means
• scikit-learn silhouette
• SciPy hierarchical clustering
• scikit-learn hierarchical clustering
• scikit-learn DBSCAN
• scikit-learn OPTICS
• scikit-learn clustering comparison
• scikit-learn clustering evaluation