What is the primary method used to determine the optimal number of clusters in K

What is the primary method used to determine the optimal number of clusters in K-means?

What is the primary method used to determine the optimal number of clusters in K-means?

Step 1: Start with your data that you want to cluster using K-means.
Step 2: Choose a range of numbers for the number of clusters (K), for example, from 1 to 10.
Step 3: For each value of K, run the K-means algorithm on your data.
Step 4: Calculate the explained variance (or inertia) for each K. This measures how well the clusters fit the data.
Step 5: Create a plot with the number of clusters (K) on the x-axis and the explained variance on the y-axis.
Step 6: Look for a point on the plot where the explained variance starts to level off, forming an 'elbow' shape.
Step 7: The number of clusters at this elbow point is considered the optimal number of clusters.

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