Exam AWS Certified Machine Learning - Specialty topic 1 question 221 discussion

The Answer is D

Option D uses the elbow method, which is a popular and well-known method for determining the optimal value of k for k-means clustering1. It plots the sum of squared errors (SSE) for different values of k, and looks for the point where the SSE starts to decrease in a linear fashion. This point is called the elbow, and it indicates that adding more clusters does not improve the model significantly2.

The Sum of square shows variation within each cluster

D. Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of squared errors (SSE). Plot a line chart of the SSE for each value of k. The optimal value of k is the point after which the curve starts decreasing in a linear fashion. The sum of squared errors (SSE) measures the total variation within each cluster, and the optimal value of k is typically the point where the SSE begins to level off or decrease sharply. Plotting the SSE against the number of clusters (k) allows the data scientist to identify the optimal number of clusters based on where the SSE curve starts decreasing linearly.

d https://towardsdatascience.com/explain-ml-in-a-simple-way-k-means-clustering-e925d019743b