Clustering

15. Clustering#

This chapter introduces clustering as unsupervised learning. You will use k-means to group observations, interpret cluster centers, and evaluate whether the resulting groups make practical sense.

Chapter Video

Video credit: StatQuest with Josh Starmer. K-means clustering. YouTube. Watch on YouTube.

This embedded video is provided for educational reference only. All rights are retained by the respective copyright holder.

Learning Objectives

By the end of this chapter, you will be able to:

Explain clustering as grouping observations without known labels.
Describe the k-means algorithm in terms of centers and assignments.
Prepare numeric features so distance-based clusters are meaningful.
Interpret cluster centers and cluster membership in an applied context.
Evaluate clusters with metrics and domain judgment.

Chapter Flow

Clustering Concepts

K-Means Workflow

Interpreting Clusters

Glossary

#	Term	Explanation
1	Clustering	Grouping observations based on similarity.
2	Unsupervised learning	Learning patterns from data without known target labels.
3	Cluster	A group of observations that are similar to each other.
4	K-means	A clustering algorithm that partitions data into k groups.
5	Centroid	The center point of a k-means cluster.
6	Assignment step	The k-means step that assigns observations to nearest centroids.
7	Update step	The k-means step that recalculates centroids.
8	Distance metric	A rule for measuring how far observations are apart.
9	Euclidean distance	Straight-line distance between points.
10	Feature scaling	Transforming variables so their scales are comparable.
11	Standardization	Scaling values by subtracting a mean and dividing by a standard deviation.
12	Within-cluster variation	How spread out points are inside a cluster.
13	Silhouette score	A metric comparing within-cluster fit with separation from other clusters.
14	Elbow method	A heuristic for choosing k by inspecting improvement as k increases.
15	Initialization	The starting placement of cluster centers.
16	Convergence	The point when assignments and centers stop changing much.
17	Dimensionality	The number of features used to represent observations.
18	Segmentation	Using clusters to divide customers, products, or cases into groups.
19	Interpretability	How easily cluster results can be explained.
20	Domain judgment	Subject-matter reasoning used to decide whether clusters are useful.
21	Noisy feature	A variable that adds distraction rather than useful grouping information.