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In a K-means clustering algorithm, if you have 5 clusters and 100 data points, h

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Question: In a K-means clustering algorithm, if you have 5 clusters and 100 data points, how many centroids will be initialized?

Options:

  1. 5
  2. 100
  3. 50
  4. 10

Correct Answer: 5

Solution: 

In K-means clustering, the number of centroids initialized is equal to the number of clusters. Therefore, if there are 5 clusters, 5 centroids will be initialized.

In a K-means clustering algorithm, if you have 5 clusters and 100 data points, h

Practice Questions

Q1
In a K-means clustering algorithm, if you have 5 clusters and 100 data points, how many centroids will be initialized?
  1. 5
  2. 100
  3. 50
  4. 10

Questions & Step-by-Step Solutions

In a K-means clustering algorithm, if you have 5 clusters and 100 data points, how many centroids will be initialized?
Correct Answer: 5
  • Step 1: Understand what K-means clustering is. It is a method used to group data points into clusters.
  • Step 2: Identify how many clusters you want to create. In this case, it is 5 clusters.
  • Step 3: Know that in K-means, each cluster has a center point called a centroid.
  • Step 4: Realize that the number of centroids is the same as the number of clusters.
  • Step 5: Since there are 5 clusters, you will initialize 5 centroids.
  • K-means Clustering – K-means clustering is an unsupervised machine learning algorithm that partitions data into K distinct clusters based on feature similarity.
  • Centroids – Centroids are the central points of each cluster in K-means, and their number is determined by the number of clusters specified.
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