Participate in this quiz to evaluate your understanding of Distance Measures used within Clustering in Unsupervised Learning.

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Participate in this quiz to evaluate your understanding of Distance Measures used within Clustering in Unsupervised Learning.

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- Question 1 of 10
##### 1. Question

**What is the Euclidean distance between two points (x1, y1) and (x2, y2) in a two-dimensional space?**CorrectIncorrect - Question 2 of 10
##### 2. Question

**What is the Manhattan distance between two points (x1, y1) and (x2, y2) in a two-dimensional space?**CorrectIncorrect - Question 3 of 10
##### 3. Question

**For binary attributes, which distance measure is most appropriate?**CorrectIncorrect - Question 4 of 10
##### 4. Question

**What is the advantage of using cosine similarity over Euclidean distance for text data in clustering?**CorrectIncorrect - Question 5 of 10
##### 5. Question

**What is the formula for Jaccard distance for two sets A and B?**CorrectIncorrect - Question 6 of 10
##### 6. Question

**In clustering, the Manhattan distance is also known as:**CorrectIncorrect - Question 7 of 10
##### 7. Question

**What does the cosine similarity measure?**CorrectIncorrect - Question 8 of 10
##### 8. Question

**Which distance measure is invariant to scale and translation?**CorrectIncorrect - Question 9 of 10
##### 9. Question

**When applying the Hamming distance to compare binary strings of different lengths, how is the dissimilarity computed?**CorrectIncorrect - Question 10 of 10
##### 10. Question

**The curse of dimensionality refers to the phenomenon where which distance measure becomes less effective as the data dimensionality increases?**CorrectIncorrect

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