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What is the primary method used to determine the optimal number of clusters in K

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Question: What is the primary method used to determine the optimal number of clusters in K-means?

Options:

  1. Elbow method
  2. Silhouette analysis
  3. Cross-validation
  4. Grid search

Correct Answer: Elbow method

Solution: 

The elbow method is a common technique used to determine the optimal number of clusters in K-means by plotting the explained variance against the number of clusters.

What is the primary method used to determine the optimal number of clusters in K

Practice Questions

Q1
What is the primary method used to determine the optimal number of clusters in K-means?
  1. Elbow method
  2. Silhouette analysis
  3. Cross-validation
  4. Grid search

Questions & Step-by-Step Solutions

What is the primary method used to determine the optimal number of clusters in K-means?
  • Step 1: Start with your data that you want to cluster using K-means.
  • Step 2: Choose a range of numbers for the number of clusters (K), for example, from 1 to 10.
  • Step 3: For each value of K, run the K-means algorithm on your data.
  • Step 4: Calculate the explained variance (or inertia) for each K. This measures how well the clusters fit the data.
  • Step 5: Create a plot with the number of clusters (K) on the x-axis and the explained variance on the y-axis.
  • Step 6: Look for a point on the plot where the explained variance starts to level off, forming an 'elbow' shape.
  • Step 7: The number of clusters at this elbow point is considered the optimal number of clusters.
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