Data Science FREE Data Science Unsupervised Learning Techniques Questions and Answers 2 — Questions and Answers
Question 1: Which unsupervised learning method groups data points by iteratively assigning them to the nearest centroid and updating centroids until convergence?
- K-Means Clustering (Correct answer)
- Principal Component Analysis
- Apriori Algorithm
- Isolation Forest
Correct answer: K-Means Clustering
K-Means works by alternating between assigning points to the closest centroid and recalculating centroids until assignments stabilize.
Question 2: What is the primary purpose of the elbow method in unsupervised learning?
- Determining the optimal number of clusters (Correct answer)
- Selecting the best features for dimensionality reduction
- Evaluating the accuracy of an anomaly detector
- Choosing the learning rate for autoencoders
Correct answer: Determining the optimal number of clusters
The elbow method plots within-cluster sum of squares against the number of clusters to identify where adding more clusters yields diminishing returns.
Question 3: In DBSCAN, what happens to data points that are neither core points nor reachable from any core point?
- They are labeled as noise (Correct answer)
- They form their own cluster
- They are assigned to the nearest cluster
- They are removed from the dataset
Correct answer: They are labeled as noise
DBSCAN classifies points that cannot be reached from any core point within the epsilon neighborhood as noise or outliers.
Question 4: Which technique reduces dimensionality by finding a lower-dimensional manifold that preserves local neighbor distances?
- t-SNE (Correct answer)
- K-Means
- Agglomerative Clustering
- Association Rule Mining
Correct answer: t-SNE
t-SNE preserves local pairwise distances when projecting high-dimensional data into two or three dimensions for visualization.
Question 5: What does the silhouette score measure in cluster analysis?
- How similar a point is to its own cluster compared to the nearest neighboring cluster (Correct answer)
- The total variance explained by the clustering
- The number of outliers detected in the dataset
- The computational complexity of the clustering algorithm
Correct answer: How similar a point is to its own cluster compared to the nearest neighboring cluster
The silhouette score ranges from -1 to 1 and compares intra-cluster cohesion with inter-cluster separation for each data point.
Question 6: Which unsupervised technique is used to discover frequent itemsets and generate rules such as 'customers who buy bread often buy butter'?
- Association Rule Mining (Correct answer)
- Gaussian Mixture Models
- Hierarchical Clustering
- Self-Organizing Maps
Correct answer: Association Rule Mining
Association Rule Mining, including algorithms like Apriori and FP-Growth, identifies frequent co-occurring itemsets and derives confidence-based rules.
Data Science FREE Data Science Unsupervised Learning Techniques Questions and Answers 2
Which unsupervised learning method groups data points by iteratively assigning them to the nearest centroid and updating centroids until convergence?