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Quiz on Hierarchical Clustering in Data Science

Quiz on Hierarchical Clustering in Data Science

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Question 1 of 10

1. Question

What is the basic idea behind hierarchical clustering?

Predict output using labels
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Merge or split clusters based on distance
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Assign probabilities to each cluster
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Reduce features of the dataset
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Question 2 of 10

2. Question

Which of the following methods is used in hierarchical clustering?

Backpropagation
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Principal Component Analysis
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Agglomerative method
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Naive Bayes
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Question 3 of 10

3. Question

What does a dendrogram represent in hierarchical clustering?

A decision path
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A classification tree
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A visual of how clusters are formed
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A time-series plot
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Question 4 of 10

4. Question

What is the most commonly used linkage method in hierarchical clustering?

Average linkage
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Complete linkage
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Single linkage
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All of the above
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Question 5 of 10

5. Question

In agglomerative clustering, how does the algorithm start?

All data in one cluster
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Randomly selected centroids
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Each data point as its own cluster
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With fixed labels
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Question 6 of 10

6. Question

What is the key difference between agglomerative and divisive clustering?

One uses labels, the other doesn’t
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One starts with all points in one cluster, the other with each point in its own
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One is supervised, the other is not
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Both are the same
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Question 7 of 10

7. Question

Which of the following is a disadvantage of hierarchical clustering?

Cannot handle numeric data
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Cannot visualize results
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High computational cost for large datasets
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Requires labeled data
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Question 8 of 10

8. Question

What does ‘linkage’ mean in hierarchical clustering?

Method to combine datasets
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Rule to calculate distance between clusters
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Labeling of the final clusters
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Technique to clean data
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Question 9 of 10

9. Question

What type of data is best suited for hierarchical clustering?

Only labeled data
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Structured and small datasets
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Video data
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Any large-scale image data
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Question 10 of 10

10. Question

Which of the following is not a characteristic of hierarchical clustering?

Does not require the number of clusters in advance
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Can be visualized using a dendrogram
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Works well with huge datasets
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Groups data based on similarity
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