Practice - Gaussian Mixture Models (GMM): A Probabilistic Approach to Clustering
Practice Questions
Test your understanding with targeted questions
What is the main difference between GMMs and K-Means clustering?
💡 Hint: Think about how clusters are formed in each method.
Define what a Gaussian distribution is.
💡 Hint: Recall the bell curve shape.
4 more questions available
Interactive Quizzes
Quick quizzes to reinforce your learning
What is one key advantage of using GMMs over K-Means clustering?
💡 Hint: Think about cluster shapes that GMMs can model.
True or False: GMMs provide hard assignments of data points to clusters.
💡 Hint: Recall how GMMs treat data points regarding cluster membership.
Get performance evaluation
Challenge Problems
Push your limits with advanced challenges
Suppose you conduct a clustering analysis and observe that GMMs report more stable results than K-Means in partitioning customer data. Discuss the potential reasons for this stability.
💡 Hint: Consider the implications of soft versus hard assignments.
Build a small example dataset with 2D points representing two clusters of different shapes. Explain how GMM would approach clustering these points compared to K-Means.
💡 Hint: Visualize the clusters and think about what shape they take.
Get performance evaluation
Reference links
Supplementary resources to enhance your learning experience.