How do you choose the number of clusters k in K-Means?

By using the Elbow Method: plotting inertia against values of k and selecting the point where the rate of decrease bends sharply (the elbow).

Home ASSEB AI HubUnit 8: Unsupervised Algorithms

Class XIASSEB-527Unit 8: Unsupervised Algorithms

Unit 8: Unsupervised Algorithms

Master official ASSEB Class XI AI Unit 8: Unsupervised learning, K-Means clustering, centroid optimization, and Principal Component Analysis (PCA) dimensionality reduction.

Theory: 4 Marks

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Practical: 7 Marks

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10 min read

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U1Employability Skills - Unit 1: ICT SkillsFree U2Employability Skills - Unit 2: Entrepreneurial Skills4m U3Employability Skills - Unit 3: Green Skills2m U1Introduction to Artificial IntelligenceFree U2AI Paradigms5m U3Mathematics for AI5m U4Programming for AI5m U5Machine Learning Paradigms4m U6Data Collection and Exploration6m U7Supervised Algorithms5m U8Unsupervised Algorithms4m U9Storytelling with Data5m U10AI Ethics and Values4m U11Case Studies & Project4m

Learning Outcomes

Understand the principles of Unsupervised Learning and uncovering natural groupings without external labels.
Implement data clustering using the K-Means algorithm (centroid initialization, assignment, update loop).
Understand dimensionality reduction and the curse of dimensionality in high-dimensional datasets.
Implement Principal Component Analysis (PCA) to compress correlated variables into orthogonal components.

Board Exam Scoring Tips

In 3-mark questions, describe the 3 steps of the K-Means algorithm: 1) Initialize k centroids, 2) Assign points to nearest centroid, 3) Recompute centroid coordinates as cluster means.
Explain the purpose of PCA: Dimensionality reduction while preserving the maximum possible variance.

Key Takeaways

Unsupervised learning operates without ground truth target labels (y), finding inherent geometric patterns in feature space X.
K-Means clustering iteratively minimizes the within-cluster sum of squared distances (inertia).
Principal Component Analysis (PCA) finds principal axes that capture the maximum variance of the original dataset.

Practice Unit MCQ Quiz

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