What is the purpose of the elbow method in K-means clustering?
Practice Questions
1 question
Q1
What is the purpose of the elbow method in K-means clustering?
To determine the optimal number of clusters
To visualize the clusters formed
To assess the performance of the algorithm
To preprocess the data before clustering
The elbow method is used to determine the optimal number of clusters by plotting the explained variance as a function of the number of clusters and identifying the 'elbow' point.
Questions & Step-by-step Solutions
1 item
Q
Q: What is the purpose of the elbow method in K-means clustering?
Solution: The elbow method is used to determine the optimal number of clusters by plotting the explained variance as a function of the number of clusters and identifying the 'elbow' point.
Steps: 8
Step 1: Understand that K-means clustering is a way to group data into clusters.
Step 2: Know that we need to decide how many clusters to use for K-means.
Step 3: The elbow method helps us find the best number of clusters.
Step 4: To use the elbow method, we run K-means with different numbers of clusters (like 1, 2, 3, etc.).
Step 5: For each number of clusters, we calculate how well the clusters fit the data (this is called explained variance).
Step 6: We create a graph with the number of clusters on the x-axis and the explained variance on the y-axis.
Step 7: Look for a point on the graph where the line starts to bend or 'elbow'.
Step 8: The number of clusters at this elbow point is considered the optimal number.
