What is the main criterion for determining the optimal number of clusters in K-m

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Question: What is the main criterion for determining the optimal number of clusters in K-means?

Options:

Correct Answer: Both A and B

Solution:

Both the Silhouette score and the Elbow method are commonly used criteria for determining the optimal number of clusters in K-means clustering.

What is the main criterion for determining the optimal number of clusters in K-means?

What is the main criterion for determining the optimal number of clusters in K-means?

Correct Answer: Silhouette score and Elbow method

Step 1: Understand that K-means is a method used to group data into clusters.
Step 2: Know that we need to decide how many clusters (groups) to create.
Step 3: Learn about the Silhouette score, which measures how similar an object is to its own cluster compared to other clusters.
Step 4: Understand that a higher Silhouette score means better-defined clusters.
Step 5: Learn about the Elbow method, which involves plotting the number of clusters against the sum of squared distances from each point to its assigned cluster center.
Step 6: Look for a point on the plot where adding more clusters doesn't significantly reduce the distance (this is the 'elbow').
Step 7: Use either the Silhouette score or the Elbow method to help decide the best number of clusters for your data.

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