Practice - Week 9: Clustering Techniques
Practice Questions
Test your understanding with targeted questions
What is K-Means clustering?
💡 Hint: Think about how points are grouped based on their proximity.
Name the three types of points in DBSCAN.
💡 Hint: Consider how each point is classified based on density.
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Interactive Quizzes
Quick quizzes to reinforce your learning
What does unsupervised learning allow models to do?
💡 Hint: Consider what type of data guides the analysis.
Is K-Means sensitive to initial centroid placement?
💡 Hint: Reflect on whether the starting conditions impact the results.
1 more question available
Challenge Problems
Push your limits with advanced challenges
Given a dataset with varying densities, how would you select an appropriate clustering algorithm, and why?
💡 Hint: Consider how different algorithms react to challenges in data structure.
You have a dendrogram from hierarchical clustering showing multiple merges. Discuss how you would decide where to make the cut for clusters.
💡 Hint: Look for distance thresholds in the dendrogram that indicate promising cluster arrangements.
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Reference links
Supplementary resources to enhance your learning experience.