praxis.ibLog in
A4.3 Machine learning approachesA4.3.4

Clustering techniques in unsupervised learning

Describe how clustering techniques in unsupervised learning are used to group data based on similarities in features

AHL≈ 55 minCommand term: DescribePremium

In this lesson

  • Describing how clustering groups unlabelled data by the similarity of its features
  • Describing k-means, hierarchical clustering, DBSCAN and mean shift clustering
  • Describing how clustering of purchasing data is used to segment a customer base

This lesson is part of Premium

Every topic has one free lesson. Premium unlocks every interactive lesson, the exam-style questions at the end of each one, and the rest of Praxis IB.

Log in or sign upTry the free lesson: A4.3.1 Linear regression for predicting continuous outcomes

What's inside

  1. LearnGrouping by similarity
  2. LearnFour clustering techniques
  3. LabFour algorithms, three data sets
  4. CheckOrder the steps
  5. CheckQuick check
  6. LearnApplication: segmenting customers
  7. CheckMatch each technique to a property
  8. ExamWorked exam answer
  9. ReviewMistakes examiners see
  10. Exam practiceExam-style questions

Ends with 5 exam-style questions worth 12 marks, with a self-marking markscheme.