Chapter 8: Clustering

Clustering is the quintessential unsupervised learning task: finding natural groupings in data without any labels to guide us. Unlike supervised learning where we have ground truth, clustering must discover structure purely from the data itself.

The applications are everywhere: segmenting customers by behavior, grouping similar documents, detecting anomalies that don't fit any cluster, compressing images by grouping similar colors, and exploring high-dimensional data by identifying natural categories.

Different clustering algorithms make different assumptions about what constitutes a "cluster." K-Means assumes clusters are spherical and roughly equal-sized. Hierarchical methods build trees that can be cut at any level. Density-based methods like DBSCAN find clusters of arbitrary shape and automatically identify outliers.

Choosing the right algorithm and parameters requires understanding these assumptions and evaluating results. Unlike classification, we can't simply measure accuracy. Instead, we use metrics like silhouette score that measure how well-separated clusters are, or domain knowledge to assess whether discovered groups are meaningful.

This chapter covers:

• K-Means: Fast partitioning algorithm that iteratively assigns points to nearest centroids
• Hierarchical: Bottom-up or top-down methods that produce dendrograms showing cluster relationships
• DBSCAN: Density-based algorithm that finds clusters of any shape and identifies noise points
• Evaluation: Metrics like silhouette score and methods like the elbow plot to assess clustering quality