What is the purpose of the elbow method in K-means clustering?
Correct Answer: The elbow method helps in finding the best number of clusters for K-means.
- 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.
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