Machine Learning Clustering 5

Question 1

Which distance metric is most appropriate for clustering text documents represented as TF-IDF vectors?

Accepted Answer

Cosine similarity

Answer

Cosine similarity measures the angle between vectors, making it robust to document length differences common in TF-IDF representations.

Question 2

What is the primary advantage of using mini-batch K-Means over standard K-Means?

Accepted Answer

It scales to very large datasets by using random subsets per iteration

Answer

Mini-batch K-Means updates centroids using random subsets (mini-batches) of data, making it much faster on large datasets with minimal accuracy loss.

Question 3

When the Davies-Bouldin Index is used to evaluate clustering quality, what does a lower value indicate?

Accepted Answer

Better clustering with compact and well-separated clusters

Answer

A lower Davies-Bouldin Index means clusters are more compact internally and more separated from each other, indicating better clustering.

Question 4

Which statement about the K-Medoids algorithm is correct compared to K-Means?

Accepted Answer

K-Medoids centroids must be actual data points

Answer

In K-Medoids, the representative of each cluster (medoid) must be an actual data point, making it more robust to outliers than K-Means.

Question 5

What does a silhouette score close to -1 for a data point indicate?

Accepted Answer

The point is likely assigned to the wrong cluster

Answer

A silhouette score near -1 means the point is much closer to a neighboring cluster than its own, suggesting a misclassification.

Machine Learning Practice Test