DA0-002 Data Analysis Practice Question
A marketing team uses K-means clustering to segment customers based on purchase history. To determine the optimal number of clusters, they plot the within-cluster sum of squares (WCSS) against k and look for an elbow. What is the purpose of this method?
Answer choices
Why each option matters
Answer the question above first, then reveal the full breakdown to understand why each option is right or wrong.
Correct answer & explanation
✓
To find the point where the rate of decrease in WCSS slows down
The elbow method helps choose k where adding more clusters yields diminishing returns in reducing variance.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✓
To find the point where the rate of decrease in WCSS slows down
Why this is correct
Correct description of the elbow method.
- ✗
To identify the value of k that minimizes WCSS
Why it's wrong here
WCSS always decreases with k; the elbow identifies a good trade-off.
- ✗
To determine the initial centroids for the algorithm
Why it's wrong here
Initial centroids are chosen randomly or via k-means++.
- ✗
To ensure all clusters have equal size
Why it's wrong here
Elbow method does not enforce equal cluster sizes.
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