Practice K-means Clustering (5.4) - Unsupervised Learning & Dimensionality Reduction (Weeks 9)
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K-Means Clustering

Practice - K-Means Clustering

Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is K-Means clustering?

💡 Hint: Think about the core idea of grouping based on distance.

Question 2 Easy

Explain the steps involved in the K-Means algorithm.

💡 Hint: Consider the iterative nature of the algorithm.

1 more question available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What does K-Means clustering primarily aim to achieve?

Group data points based on similarity
Predict future outcomes
Label data points
None of the above

💡 Hint: Remember what K-Means does with data.

Question 2

The Elbow Method helps determine what?

True
False

💡 Hint: Think about cluster selection techniques.

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Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

You have a dataset with many outliers. Explain how K-Means will handle this and propose a solution.

💡 Hint: Consider how the mean is affected by extreme values.

Challenge 2 Hard

You’re tasked with clustering customer data. Describe how you would determine the optimal K and why it's important.

💡 Hint: Think about accuracy in your clustering results.

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Reference links

Supplementary resources to enhance your learning experience.