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Quiz on K-means Clustering in Data Science

Quiz on K-means Clustering in Data Science

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Question 1 of 10

1. Question

What is the main objective of the K-Means algorithm?

Classify data using a decision tree
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Group data into K clusters based on similarity
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Reduce the dimensions of the dataset
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Predict continuous values
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Question 2 of 10

2. Question

In K-Means, how are the initial centroids selected?

Manually by the user
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Based on the largest values
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Randomly from the data points
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Using supervised labels
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Question 3 of 10

3. Question

What does the algorithm do after assigning points to clusters?

Deletes duplicate records
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Computes the cost function
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Recalculates the centroids
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Sorts the data
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Question 4 of 10

4. Question

Which distance metric is commonly used in K-Means?

Cosine similarity
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Manhattan distance
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Hamming distance
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Euclidean distance
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Question 5 of 10

5. Question

What happens if the value of K is too small?

It increases accuracy
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Some clusters may overlap
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Too many clusters are created
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Important patterns may be missed
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Question 6 of 10

6. Question

Which of the following is true about K-Means?

It can only be used with labeled data
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It works well with non-spherical clusters
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It may give different results on different runs
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It assigns probabilities to each cluster
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Question 7 of 10

7. Question

How is the ‘best’ K value generally determined?

Using confusion matrix
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By running Logistic Regression
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Using the Elbow method
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By taking the mean of all features
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Question 8 of 10

8. Question

When does the K-Means algorithm stop iterating?

When the dataset is sorted
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After a fixed number of steps only
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When cluster centroids stop changing
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When new data is added
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Question 9 of 10

9. Question

What is a centroid in K-Means clustering?

A fixed label
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A cluster boundary
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The average position of all points in a cluster
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The data point with the highest value
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Question 10 of 10

10. Question

What is a major limitation of K-Means?

Works only with text data
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Requires training and test sets
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Struggles with clusters of different sizes and shapes
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Cannot be applied to numeric data
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