Machine Learning Clustering 2

Question 1

Which linkage criterion in hierarchical clustering merges clusters based on the maximum distance between any two points in different clusters?

Accepted Answer

Complete linkage

Answer

Complete linkage uses the maximum pairwise distance between clusters, producing compact, spherical clusters.

Question 2

What is the primary disadvantage of the DBSCAN algorithm?

Accepted Answer

It struggles with clusters of varying density

Answer

DBSCAN uses a fixed epsilon and minPts, making it sensitive to clusters with significantly different densities.

Question 3

In the Expectation-Maximization (EM) algorithm for Gaussian Mixture Models, what does the E-step compute?

Accepted Answer

The posterior probability of each point belonging to each component

Answer

The E-step computes the responsibility (posterior probability) of each Gaussian component for every data point.

Question 4

Which internal clustering evaluation metric measures how similar a point is to its own cluster compared to other clusters?

Accepted Answer

Silhouette Score

Answer

The Silhouette Score ranges from -1 to 1 and compares intra-cluster cohesion to inter-cluster separation for each point.

Question 5

What does 'inertia' measure in the context of K-Means clustering?

Accepted Answer

The sum of squared distances from each point to its assigned centroid

Answer

Inertia (within-cluster sum of squares) measures the compactness of clusters; lower inertia indicates tighter clusters.

Machine Learning Practice Test