MLS-C01 · Question #249
A company wants to segment a large group of customers into subgroups based on shared characteristics. The company's data scientist is planning to use the Amazon SageMaker built-in k-means clustering…
The correct answer is D. Run the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of. 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…
Question
A company wants to segment a large group of customers into subgroups based on shared characteristics. The company's data scientist is planning to use the Amazon SageMaker built-in k-means clustering algorithm for this task. The data scientist needs to determine the optimal number of subgroups (k) to use. Which data visualization approach will MOST accurately determine the optimal value of k?
Options
- ACalculate the principal component analysis (PCA) components. Run the k-means clustering
- BCalculate the principal component analysis (PCA) components. Create a line plot of the number
- CCreate a t-distributed stochastic neighbor embedding (t-SNE) plot for a range of perplexity values.
- DRun the k-means clustering algorithm for a range of k. For each value of k, calculate the sum of
How the community answered
(23 responses)- A9% (2)
- B13% (3)
- C4% (1)
- D74% (17)
Explanation
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.
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