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Unsupervised Learning Techniques
Check your understanding of clustering and dimensionality reduction in scikit-learn.
1. What is the primary objective of the K-means clustering algorithm?
Minimize the number of clusters
Maximize the distance between different clusters
Minimize the sum of squared distances from data points to their cluster centroids
Maximize the number of data points in each cluster
2. Which of the following techniques is primarily used for dimensionality reduction in unsupervised learning?
K-means
PCA (Principal Component Analysis)
Apriori
Support Vector Machines (SVM)
3. What is a key characteristic of the DBSCAN clustering algorithm?
Requires specifying the exact number of clusters in advance
Density-based clustering that identifies arbitrary-shaped clusters
Hierarchically merges clusters based on distance metrics
Relies on association rules to group data points
4. Which of the following are examples of clustering algorithms in unsupervised learning? (Select all that apply)
K-means
PCA (Principal Component Analysis)
DBSCAN
t-SNE
Hierarchical Clustering
5. Which of the following are dimensionality reduction techniques? (Select all that apply)
PCA (Principal Component Analysis)
t-SNE (t-Distributed Stochastic Neighbor Embedding)
Apriori
UMAP (Uniform Manifold Approximation and Projection)
K-means
6. Which of the following are common applications of unsupervised learning? (Select all that apply)
Customer segmentation
Image classification
Anomaly detection in network traffic
Market basket analysis
Spam detection
7. Principal Component Analysis (PCA) is an unsupervised learning technique.
True
False
8. The K-means algorithm is guaranteed to converge to the global optimum solution.
True
False
9. What does the acronym 'DBSCAN' stand for in the context of unsupervised learning?
10. Name one popular algorithm used for association rule learning in unsupervised learning.
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