MS-DS Master of Data science MS-DS Master of Data science Unsupervised Learning Techniques Questions and Answers 2 — Questions and Answers
Question 1: Which unsupervised learning technique reduces dimensionality by finding orthogonal axes that maximize variance in the data?
- Principal Component Analysis (PCA) (Correct answer)
- K-Means Clustering
- DBSCAN
- Apriori Algorithm
Correct answer: Principal Component Analysis (PCA)
PCA identifies principal components as orthogonal directions of maximum variance, enabling dimensionality reduction while preserving the most information.
Question 2: In DBSCAN, what is a 'core point'?
- A point with at least MinPts neighbors within radius epsilon (Correct answer)
- The centroid of each cluster
- A point equidistant from two cluster centers
- The first point selected during initialization
Correct answer: A point with at least MinPts neighbors within radius epsilon
DBSCAN defines a core point as one that has at least MinPts data points within its epsilon-radius neighborhood, forming the dense regions of clusters.
Question 3: What does the silhouette coefficient measure in clustering evaluation?
- How similar a point is to its own cluster compared to the nearest neighboring cluster (Correct answer)
- The total within-cluster sum of squares
- The number of optimal clusters using the elbow method
- The ratio of between-cluster variance to total variance
Correct answer: How similar a point is to its own cluster compared to the nearest neighboring cluster
The silhouette coefficient ranges from -1 to 1 and compares intra-cluster cohesion with nearest-cluster separation for each data point.
Question 4: Which technique is most appropriate for discovering hierarchical groupings without specifying the number of clusters in advance?
- Agglomerative hierarchical clustering (Correct answer)
- K-Means clustering
- Gaussian Mixture Models
- Self-Organizing Maps
Correct answer: Agglomerative hierarchical clustering
Agglomerative hierarchical clustering builds a dendrogram by iteratively merging the closest clusters, allowing the analyst to choose the number of clusters after the fact.
Question 5: In a Gaussian Mixture Model (GMM), what algorithm is typically used to estimate the model parameters?
- Expectation-Maximization (EM) (Correct answer)
- Gradient Descent
- Forward-Backward Algorithm
- Stochastic Gradient Descent
Correct answer: Expectation-Maximization (EM)
The EM algorithm iterates between computing soft cluster assignments (E-step) and updating the Gaussian parameters to maximize likelihood (M-step).
Question 6: What is the primary purpose of t-SNE in unsupervised learning?
- Visualizing high-dimensional data in two or three dimensions (Correct answer)
- Clustering data into discrete groups
- Performing feature selection
- Training an autoencoder network
Correct answer: Visualizing high-dimensional data in two or three dimensions
t-SNE is a nonlinear dimensionality reduction technique designed specifically for visualizing high-dimensional datasets in low-dimensional space while preserving local structure.
