
Understanding K-means Clustering Basics
Quiz
•
Other
•
12th Grade
•
Practice Problem
•
Hard
Shilpa M
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20 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is the first step in the K-means clustering algorithm?
Assign each data point to the nearest cluster centre
Calculate the silhouette score
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What does the 'K' in K-means represent?
The number of features in the dataset
The number of clusters to form
The number of iterations
The number of data points
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which distance metric is most commonly used in the standard K-means algorithm?
Manhattan distance
Cosine similarity
Euclidean distance
Hamming distance
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
After assigning each data point to the nearest cluster centre, what is the next step in the K-means algorithm?
Remove outliers from the dataset
Recalculate the cluster centres (means)
Calculate the silhouette score
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
The elbow method
The silhouette method
Random selection
Gap statistic
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
Which of the following is a real-world application of K-means clustering?
Predicting stock prices
Grouping customers based on purchasing behaviour
Sorting emails by date
Calculating the mean temperature
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the main objective of the K-means algorithm?
Maximise the distance between clusters
Minimise the sum of squared distances between data points and their cluster centre
Maximise the number of clusters
Minimise the number of features
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