FREE Data Science Unsupervised Learning Techniques Questions and Answers Flashcards

Which unsupervised learning method groups data points by iteratively assigning them to the nearest centroid and updating centroids until convergence?

Answer: K-Means Clustering

K-Means works by alternating between assigning points to the closest centroid and recalculating centroids until assignments stabilize.

What is the primary purpose of the elbow method in unsupervised learning?

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.

In DBSCAN, what happens to data points that are neither core points nor reachable from any core point?

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.

Which technique reduces dimensionality by finding a lower-dimensional manifold that preserves local neighbor distances?

Answer: t-SNE

t-SNE preserves local pairwise distances when projecting high-dimensional data into two or three dimensions for visualization.

What does the silhouette score measure in cluster analysis?

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.

Which unsupervised technique is used to discover frequent itemsets and generate rules such as 'customers who buy bread often buy butter'?

Answer: Association Rule Mining

Association Rule Mining, including algorithms like Apriori and FP-Growth, identifies frequent co-occurring itemsets and derives confidence-based rules.