Practical Data Science using Python - K-Means Clustering Computation
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Information Technology (IT), Architecture, Social Studies
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University
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Practice Problem
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Hard
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10 questions
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1.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the primary goal of K-Means clustering?
To find the optimal number of centroids
To predict future data points
To classify data into predefined categories
To determine the best regression line
2.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In K-Means clustering, what does the 'K' represent?
The number of iterations
The number of dimensions
The number of data points
The number of centroids
3.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is a centroid in the context of K-Means clustering?
The center point of a cluster
A data point with the highest value
The farthest point from the origin
A randomly selected data point
4.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
How is the Euclidean distance used in K-Means clustering?
To determine the distance between centroids
To calculate the average of data points
To find the maximum distance between clusters
To assign data points to the nearest centroid
5.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
What is the first step in the K-Means clustering process?
Calculate the mean of each cluster
Determine the value of K
Optimize the centroids
Assign data points to clusters
6.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
During the K-Means process, how are new centroids calculated?
By averaging the coordinates of data points in a cluster
By selecting random data points
By using the median of the data points
By choosing the farthest data point
7.
MULTIPLE CHOICE QUESTION
30 sec • 1 pt
In the worked example, what is the purpose of calculating squared distances?
To minimize the cost function
To find the largest cluster
To determine the number of clusters
To predict future data points
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