Practice K-means Clustering (6.1.2.1) - Unsupervised Learning – Clustering & Dimensionality Reduction

Practice: K-Means Clustering

Lesson 6.1.2.1Unsupervised Learning – Clustering & Dimensionality Reduction

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9 questions

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3 cards from this section. Quick, good the night before a test.
Exercise
6 questions · about 4 min · pass mark 70%. Hints available.
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Quiz
3 questions · about 2 min · pass mark 70%. Hints available.
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6 sample questions

Question 1.
What is a centroid in K-Means Clustering?
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Think about what it means to be 'central' or 'average'.
Question 2.
What does WCSS stand for?
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It’s a measure of how compact the clusters are.
3Question 3.
What is the main objective of K-Means Clustering?
- Minimize the distance between all points
- Minimize WCSS
- Maximize the distance between clusters
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Focus on what K-Means is designed to achieve!
4Question 4.
True or False: K-Means Clustering does not require the number of clusters (K) to be predefined.
- True
- False
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Remember our class discussions on K’s importance.
Question 5.
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.
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Think about the limitations of K-Means for various cluster shapes.
Question 6.
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.
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Think about preprocessing techniques that can handle skewed data.

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