DSE Unsupervised Learning: Clustering 3

Question 1

What does the Calinski-Harabasz Index (Variance Ratio Criterion) measure in clustering evaluation?

Accepted Answer

Ratio of between-cluster dispersion to within-cluster dispersion

Answer

Higher Calinski-Harabasz scores indicate dense, well-separated clusters because between-cluster variance far exceeds within-cluster variance.

Question 2

In Gaussian Mixture Models (GMM), what algorithm is used to estimate the model parameters?

Accepted Answer

Expectation-Maximization (EM)

Answer

The EM algorithm alternates between assigning soft cluster membership probabilities (E-step) and updating Gaussian parameters (M-step) until convergence.

Question 3

A dendrogram cut at a height of 5 produces 3 clusters, while a cut at height 3 produces 7 clusters. What does this tell you about the data at heights between 3 and 5?

Accepted Answer

Four merges occur between heights 3 and 5

Answer

Going from 7 clusters to 3 requires exactly 4 merge operations, each corresponding to one horizontal line in the dendrogram between those heights.

Question 4

Which parameter in DBSCAN controls the minimum density required for a region to be considered a core region?

Accepted Answer

min_samples

Answer

min_samples specifies how many points must be within the epsilon radius for a point to qualify as a core point, directly controlling density thresholds.

Question 5

What is 'cluster tendency' and why is it assessed before clustering?

Accepted Answer

Whether the data has any meaningful cluster structure

Answer

Assessing cluster tendency (e.g., via Hopkins statistic) checks if the data has non-random structure worth clustering before applying any algorithm.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE Unsupervised Learning: Clustering 3