Practice Applications of Clustering & Dimensionality Reduction - 6.3 | 6. Unsupervised Learning – Clustering & Dimensionality Reduction | Data Science Advance
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Applications of Clustering & Dimensionality Reduction

6.3 - Applications of Clustering & Dimensionality Reduction

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Learning

Practice Questions

Test your understanding with targeted questions

Question 1 Easy

What is clustering?

💡 Hint: Think about how you would group books in a library.

Question 2 Easy

Name one application of dimensionality reduction.

💡 Hint: Consider what happens when you reduce the number of dimensions.

4 more questions available

Interactive Quizzes

Quick quizzes to reinforce your learning

Question 1

What is the main purpose of clustering?

To simplify data
To group similar data points
To analyze time-series data

💡 Hint: Consider its role in data analysis.

Question 2

Is dimensionality reduction useful for visualizations?

True
False

💡 Hint: Think about how you observe complex data.

2 more questions available

Challenge Problems

Push your limits with advanced challenges

Challenge 1 Hard

Design a clustering algorithm for a fictional e-commerce website looking to enhance user experience. What features would you prioritize, and how would you interpret the clusters formed?

💡 Hint: Consider the user journey on the platform.

Challenge 2 Hard

You are given a high-dimensional dataset in bioinformatics. Discuss how you would approach dimensionality reduction and why it is essential before clustering.

💡 Hint: Reflect on the computational challenges of high-dimensional data.

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Reference links

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