Unsupervised Learning: Clustering Flashcards
7 cards from real DSE practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 7 Unsupervised Learning: Clustering flashcards as text
What does the Calinski-Harabasz Index (Variance Ratio Criterion) measure in clustering evaluation?
Answer: Ratio of between-cluster dispersion to within-cluster dispersion
Higher Calinski-Harabasz scores indicate dense, well-separated clusters because between-cluster variance far exceeds within-cluster variance.
In Gaussian Mixture Models (GMM), what algorithm is used to estimate the model parameters?
Answer: Expectation-Maximization (EM)
The EM algorithm alternates between assigning soft cluster membership probabilities (E-step) and updating Gaussian parameters (M-step) until convergence.
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?
Answer: Four merges occur between heights 3 and 5
Going from 7 clusters to 3 requires exactly 4 merge operations, each corresponding to one horizontal line in the dendrogram between those heights.
Which parameter in DBSCAN controls the minimum density required for a region to be considered a core region?
Answer: min_samples
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.
What is 'cluster tendency' and why is it assessed before clustering?
Answer: Whether the data has any meaningful cluster structure
Assessing cluster tendency (e.g., via Hopkins statistic) checks if the data has non-random structure worth clustering before applying any algorithm.
In K-Means, what is the objective function being minimized?
Answer: Total within-cluster sum of squared distances to centroids
K-Means minimizes the Within-Cluster Sum of Squares (WCSS), also called inertia, which is the sum of squared Euclidean distances from each point to its assigned centroid.
Which of the following scenarios would cause DBSCAN to classify most points as noise?
Answer: Setting min_samples too high relative to local density
When min_samples is set too high, most points will fail to meet the density threshold and be labeled as noise, even in genuinely dense regions.