DSE Unsupervised Learning: Clustering 2

Question 1

Which internal cluster validation metric measures the ratio of intra-cluster distances to inter-cluster distances?

Accepted Answer

Davies-Bouldin Index

Answer

The Davies-Bouldin Index measures the average similarity ratio of each cluster to its most similar cluster, where lower values indicate better clustering.

Question 2

In DBSCAN, a point that is reachable from a core point but is not itself a core point is called a:

Accepted Answer

Border point

Answer

Border points lie within the epsilon neighborhood of a core point but do not have enough neighbors to be core points themselves.

Question 3

What is the primary advantage of using Mini-Batch K-Means over standard K-Means?

Accepted Answer

Faster convergence on large datasets

Answer

Mini-Batch K-Means uses small random subsets of data in each iteration, dramatically reducing computation time while maintaining comparable quality.

Question 4

Which linkage criterion in hierarchical clustering tends to produce compact, spherical clusters?

Accepted Answer

Ward linkage

Answer

Ward linkage minimizes the total within-cluster variance at each merge step, consistently producing compact and similarly-sized clusters.

Question 5

A data scientist runs K-Means with k=5 and observes that one cluster has only 2 points out of 10,000. What is the most likely issue?

Accepted Answer

Poor initialization of centroids

Answer

Poor centroid initialization can trap a centroid in a sparse region, resulting in a near-empty cluster while others are overcrowded.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE Unsupervised Learning: Clustering 2