DA0-002 Data Analysis Practice Question
A dataset contains height measurements in centimeters and inches. An analyst wants to apply k-means clustering. Which data transformation should be applied before clustering?
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
✓
Min-max normalization
Min-max normalization scales features to a range, often [0,1], which is appropriate for distance-based algorithms like k-means.
Answer analysis
Option-by-option breakdown
For each option: why learners choose it and why it is or isn't the right answer here.
- ✗
Log transformation
Why it's wrong here
Log transformation handles skewness, not scaling.
- ✗
Z-score standardization
Why it's wrong here
Standardization is for algorithms assuming normality, but k-means uses distance.
- ✓
Min-max normalization
Why this is correct
Normalization ensures equal weight from all features.
- ✗
No transformation needed
Why it's wrong here
Different units would bias the distance calculation.
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