What is the main criterion for determining the optimal number of clusters in K-means?
Practice Questions
1 question
Q1
What is the main criterion for determining the optimal number of clusters in K-means?
Silhouette score
Elbow method
Both A and B
None of the above
Both the Silhouette score and the Elbow method are commonly used criteria for determining the optimal number of clusters in K-means clustering.
Questions & Step-by-step Solutions
1 item
Q
Q: What is the main criterion for determining the optimal number of clusters in K-means?
Solution: Both the Silhouette score and the Elbow method are commonly used criteria for determining the optimal number of clusters in K-means clustering.
Steps: 7
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.
