Practice Applications Of Clustering & Dimensionality Reduction (6.3) - Unsupervised Learning – Clustering & Dimensionality Reduction

Practice: Applications of Clustering & Dimensionality Reduction

Lesson 6.3Unsupervised Learning – Clustering & Dimensionality Reduction

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10 questions

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4 cards from this section. Quick, good the night before a test.
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Quiz
4 questions · about 3 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is clustering?
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Think about how you would group books in a library.
Question 2.
Name one application of dimensionality reduction.
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Consider what happens when you reduce the number of dimensions.
3Question 3.
What is the main purpose of clustering?
- To simplify data
- To group similar data points
- To analyze time-series data
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Consider its role in data analysis.
4Question 4.
Is dimensionality reduction useful for visualizations?
- True
- False
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Think about how you observe complex data.
Question 5.
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?
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Consider the user journey on the platform.
Question 6.
You are given a high-dimensional dataset in bioinformatics. Discuss how you would approach dimensionality reduction and why it is essential before clustering.
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Reflect on the computational challenges of high-dimensional data.

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