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Practice Questions
Test your understanding with targeted questions related to the topic.
Question 1
Easy
What is a centroid in K-Means Clustering?
💡 Hint: Think about what it means to be 'central' or 'average'.
Question 2
Easy
What does WCSS stand for?
💡 Hint: It’s a measure of how compact the clusters are.
Practice 4 more questions and get performance evaluation
Interactive Quizzes
Engage in quick quizzes to reinforce what you've learned and check your comprehension.
Question 1
What is the main objective of K-Means Clustering?
- Minimize the distance between all points
- Minimize WCSS
- Maximize the distance between clusters
💡 Hint: Focus on what K-Means is designed to achieve!
Question 2
True or False: K-Means Clustering does not require the number of clusters (K) to be predefined.
- True
- False
💡 Hint: Remember our class discussions on K’s importance.
Solve 1 more question and get performance evaluation
Challenge Problems
Push your limits with challenges.
Question 1
You are given a dataset with 2-dimensional data arranged in a circular pattern. Discuss how K-Means might struggle with such a dataset and propose alternative clustering methods.
💡 Hint: Think about the limitations of K-Means for various cluster shapes.
Question 2
Consider a dataset with significant outliers and a desired number of K as 3. Propose a preprocessing step to improve K-Means performance on this data.
💡 Hint: Think about preprocessing techniques that can handle skewed data.
Challenge and get performance evaluation