Computer Science · A4.3 Machine learning approaches (HL only)

A4.3.4 — Clustering: Grouping Data by Similarity

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IB DP Computer Science — A4 Machine learning — Machine learning approaches | HL | A4.3.4

📋 What You Will Learn

  • Describe how clustering groups unlabelled data based on similarities in features
  • Distinguish unsupervised clustering from supervised classification
  • Describe the k-means algorithm: assign to nearest centroid, recompute, repeat
  • Trace k-means by hand on a small 2D dataset until it converges
  • Describe real-world applications, especially customer segmentation from purchasing data
  • Identify limitations: choosing k, sensitivity to starting points and scaling

🧲 Why This Matters Tip 10,000 mixed coins onto a table and, without any labels, you would still sort them into piles — same size and colour together. Clustering does the same with data: no one tells the algorithm what the groups are; it discovers them because similar rows sit near each other in feature space.

Unsupervised Learning: No Labels, Find Structure

  • Clustering is unsupervised : the training data has no class labels and no "correct answers". Contrast

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More notes on A4.3 Machine learning approaches (HL only)

  1. 01A4.3.2 — Classification: K-Nearest Neighbours and Decision Trees
  2. 02A4.3.3 — Hyperparameter Tuning and Evaluating Supervised Models
  3. 03A4.3.8 — Artificial Neural Networks: Perceptrons to Multi-Layer Networks
  4. 04A4.3.10 — Model Selection and Comparison in Machine Learning
  5. 05A4.3.7 — Genetic Algorithms: Evolution as an Optimiser
  6. 06A4.3.5 — Association Rule Learning: Uncovering Relationships in Data
  7. 07A4.3.9 — Convolutional Neural Networks: Learning Spatial Hierarchies
  8. 08A4.3.6 — Reinforcement Learning: Agents, Rewards and Policies
  9. 09A4.3.1 — Linear Regression: Predicting Continuous Outcomes