Intermediate · Fundamentals

Clustering

Visual diagram · (in preparation) · Math · (in preparation) · Worked example · 3 difficulty levels.

TL;DR. An unsupervised technique that groups similar data points without using labels.

Technical Definition

An unsupervised technique that groups similar data points without using labels.

How it works

Clustering discovers structure in unlabeled data — customer segments, document topics, gene families. K-means partitions data into k groups by minimizing intra-cluster distance. DBSCAN finds dense regions of arbitrary shape. Hierarchical clustering builds a tree of nested groups. Choosing the number of clusters and a meaningful distance metric are core challenges.

Related Concepts

• Feature Engineering — The process of creating, selecting, and transforming input variables to improve a machine learning model's performance.
• Unsupervised Learning — Learning patterns from data that has no labels — only the inputs.