Which method can be used to determine the optimal number of clusters in K-Means?

Which method can be used to determine the optimal number of clusters in K-Means?

Step 1: Run the K-Means algorithm with a range of cluster numbers (e.g., from 1 to 10).
Step 2: For each number of clusters, calculate the explained variance (also known as inertia or within-cluster sum of squares).
Step 3: Create a plot with the number of clusters on the x-axis and the explained variance on the y-axis.
Step 4: Look for a point on the plot where the explained variance starts to decrease at a slower rate. This point is called the 'elbow'.
Step 5: The number of clusters at the elbow point is considered the optimal number of clusters.

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