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

A4.3.5 — Association Rule Learning: Uncovering Relationships in Data

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IB DP Computer Science — A4 Machine learning — Machine learning approaches | HL | A4.3.5

📋 What You Will Learn

  • Describe what an association rule (IF A THEN B) expresses about attributes in a large data set
  • Calculate and interpret support, confidence and lift for a rule
  • Describe how mining techniques (the Apriori idea) find frequent itemsets efficiently
  • Construct rules from a small transaction table by hand
  • Interpret mined rules for a given scenario, including crime analysis and predictive policing
  • Identify pitfalls: association is not causation; low-lift rules mislead

🧺 Why This Matters Supermarkets discovered decades ago that certain items keep landing in the same basket. Rules like "customers who buy bread also tend to buy butter" were mined from millions of till receipts — and they now drive shelf layout, bundle offers and "frequently bought together" boxes. The same mining works on any data set of co-occurring attributes, from shopping baskets to crime reports.

What Association Rule Learning Does

  • It is

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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. 06A4.3.9 — Convolutional Neural Networks: Learning Spatial Hierarchies
  7. 07A4.3.6 — Reinforcement Learning: Agents, Rewards and Policies
  8. 08A4.3.1 — Linear Regression: Predicting Continuous Outcomes
  9. 09A4.3.4 — Clustering: Grouping Data by Similarity