DSE Unsupervised Learning: Clustering 4

Question 1

What is the key difference between soft clustering and hard clustering?

Accepted Answer

Soft clustering assigns probabilities of membership to multiple clusters; hard clustering assigns each point to exactly one cluster

Answer

In soft (fuzzy) clustering like GMM, each point has a fractional membership probability across all clusters, unlike hard clustering where membership is binary.

Question 2

When applying K-Means to text data represented as TF-IDF vectors, which distance metric is typically preferred?

Accepted Answer

Cosine similarity

Answer

Cosine similarity measures the angle between document vectors regardless of magnitude, making it more appropriate for high-dimensional sparse TF-IDF representations.

Question 3

What does the 'elbow' in the K-Means elbow method represent?

Accepted Answer

The k value where marginal reduction in inertia becomes small

Answer

The elbow is where adding more clusters yields diminishing returns in inertia reduction, suggesting that additional clusters explain little additional variance.

Question 4

OPTICS is an extension of DBSCAN that addresses which of its main limitations?

Accepted Answer

DBSCAN struggles with clusters of varying density

Answer

OPTICS produces a reachability plot that reveals clusters at multiple density levels, overcoming DBSCAN's single global epsilon that fails when clusters have different densities.

Question 5

A customer segmentation model produces 6 clusters, but cluster 3 has a silhouette score of -0.2 while others average 0.6. What should the analyst do?

Accepted Answer

Try a different k value or merge cluster 3 with its nearest cluster

Answer

A negative silhouette score means points in cluster 3 are on average closer to another cluster than their own, indicating a poor cluster assignment that warrants reconfiguration.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE Unsupervised Learning: Clustering 4